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Abstract. A fundamental issue associated with the dynamical downscaling technique using limited-area models is related to the presence of a “spatial spin-up” belt close to the lateral boundaries where small-scale features are only partially developed. Here, we introduce a method to identify the distance from the border that is affected by the spatial spin-up (i.e., the spatial spin-up distance) of the precipitation field in convection-permitting model (CPM) simulations. Using a domain over eastern North America, this new method is applied to several simulations that differ on the nesting approach (single or double nesting) and the 3-D variables used to drive the CPM simulation. Our findings highlight three key points. Firstly, when using a single nesting approach, the spin-up distance from lateral boundaries can extend up to 300 km (around 120 CPM grid points), varying across seasons, boundaries and driving variables. Secondly, the greatest spin-up distances occur in winter at the western and southern boundaries, likely due to strong atmospheric inflow during these seasons. Thirdly, employing a double nesting approach with a comprehensive set of microphysical variables to drive CPM simulations offers clear advantages. The computational gains from reducing spatial spin-up outweigh the costs associated with the more demanding intermediate simulation of the double nesting. These results have practical implications for optimizing CPM simulation configurations, encompassing domain selection and driving strategies.

Abstract 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.

Numerous empirical studies have demonstrated that street trees not only reduce dust pollution and absorb particulate matter (PM) but also improve microclimates, providing both ecological functions and aesthetic value. However, recent research has revealed that street tree canopy cover can impede the dispersion of atmospheric PM within street canyons, leading to the accumulation of street pollutants. Although many studies have investigated the impact of street trees on air pollutant dispersion within street canyons, the extent of their influence remains unclear and uncertain. Pollutant accumulation corresponds to the specific characteristics of individual street canyons, coupled with meteorological factors and pollution source strength. Notably, the characteristics of street tree canopy cover also exert a significant influence. There is still a quantitative research gap on street tree cover impacts with respect to pollution and dust reduction control measures within street spaces. To improve urban traffic environments, policymakers have mainly focused on scientifically based street vegetation deployment initiatives in building ecological garden cities and improving the living environment. To address uncertainties regarding the influence of street trees on the dispersion of atmospheric PM in urban streets, this study reviews dispersion mechanisms and key atmospheric PM factors in urban streets, summarizes the research approaches used to conceptualize atmospheric PM dispersion in urban street canyons, and examines urban plant efficiency in reducing atmospheric PM. Furthermore, we also address current challenges and future directions in this field to provide a more comprehensive understanding of atmospheric PM dispersion in urban streets and the role that street trees play in mitigating air pollution.

Abstract The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.

Abstract This study presents a firm‐specific methodology for extracting implied default intensities and recovery rates jointly from unit recovery claim prices—backed by out‐of‐the‐money put options—and credit default swap premiums, therefore providing time‐varying and market‐consistent views of credit risk at the individual level. We apply the procedure to about 400 firms spanning different sectors of the US economy between 2003 and 2019. The main determinants of default intensities and recovery rates are analyzed with statistical and machine learning methods linking default risk and credit losses to market, sector, and individual variables. Consistent with the literature, we find that individual volatility, leverage, and corporate bond market determinants are key factors explaining the implied default intensities and recovery rates. Then, we apply the framework in the context of credit risk management in applications, like, market‐consistent credit value‐at‐risk calculation and stress testing.

Abstract There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects. In this paper, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States (US). The underlying flood occurrence model quantifies the financial impacts of short-term (12–48 h) precipitation dynamics over the present (2010–2030) and future climate (2040–2060) by leveraging statistical/machine learning and regional climate models. The flood occurrence model is designed for applications that do not require street-level precision as is often the case for scenario and trend analyses. It is applied at the full scale of Canada and the US over 10–25 km grids. Our analyses show that climate change and urbanization will typically increase losses over Canada and the US, while impacts are strongly heterogeneous from one state or province to another, or even within a territory. Portfolio applications highlight the importance for a (re)insurer to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.

Abstract Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a significant methodological challenge. It is therefore of high societal value to synthetically simulate TC tracks and winds to assess potential impacts along with their probability distributions for example, land use planning and financial risk management. A common approach to generate TC tracks is to apply storm detection methodologies to climate model output, but such an approach is sensitive to the method and parameterization used and tends to underestimate intense TCs. We present a global TC model (the UQAM‐TCW model thereafter) that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track for the six typical cyclogenesis basins from IBTrACS. Being an important driver of TCs globally, we also integrate ENSO effects in key components of the model. The global TC model thus belongs to a recent strand of literature that combines probabilistic and physical approaches to TC track generation. As an application of the model, we show global hazard maps for direct and indirect hits expressed in terms of return periods. The global TC model can be of interest to climate and environmental scientists, economists and financial risk managers. , Plain Language Summary Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a difficult task. Being able to randomly simulate TCs and their features (such as wind speed) with mathematical models is therefore critical to build scenarios (and their corresponding probability) for land use planning and financial risk management. A common approach is to simulate TCs by tracking them directly in climate model outputs but this often underestimates the frequency of intense TCs while being computationally costly overall to generate a large number of events. For these reasons, many authors have looked into alternative approaches that replicate key physical features of TCs but rather using statistical models that are much less computationally demanding. This paper therefore presents a global TC model that leverages the strengths of both statistical and climate models to simulate a large number of TCs whose features are consistent with the physics and observations. As an important global phenomenon that affects TCs globally, we also integrate in our model the effects of El Niño. The paper focuses on the methodology and validation of each model component and concludes with global hazard maps for direct and indirect hits. , Key Points We present a global tropical cyclone (TC) wind model built upon a climate model large ensemble that can be used for risk analysis We integrate ENSO into our model since it is a strong driver of storm annual frequency, cyclogenesis, trajectories, and intensity We present global hazard maps consistent with statistical features of TC components and coherent with a global climate model

Abstract With over one‐third of terrestrial net primary productivity transferring to the litter layer annually, the carbon release from litter serves as a crucial valve in atmospheric carbon dioxide concentrations. However, few quantitative global projections of litter carbon release rate in response to climate change exist. Here, we combined a global foliar litter carbon release dataset (8973 samples) to generate spatially explicitly estimates of the response of their residence time ( τ ) to climate change. Results show a global mean litter carbon release rate () of 0.69 year −1 (ranging from 0.09–5.6 year −1 ). Under future climate scenarios, global mean τ is projected to decrease by a mean of 2.7% (SSP 1–2.6) and 5.9% (SSP 5–8.5) during 2071–2100 period. Locally, the alleviation of temperature and moisture restrictions corresponded to obvious decreases in τ in cold and arid regions, respectively. In contract, τ in tropical humid broadleaf forests increased by 4.6% under SSP 5–8.5. Our findings highlight the vegetation type as a powerful proxy for explaining global patterns in foliar litter carbon release rates and the role of climate conditions in predicting responses of carbon release to climate change. Our observation‐based estimates could refine carbon cycle parameterization, improving projections of carbon cycle–climate feedbacks.

Abstract Meteorological processes over islands with complex orography could be better simulated by Convection Permitting Regional Climate Models (CP-RCMs) thanks to an improved representation of the orography, land–sea contrasts, the combination of coastal and orographic effects, and explicit deep convection. This paper evaluates the ability of the CP-RCM CNRM-AROME (2.5-km horizontal resolution) to simulate relevant meteorological characteristics of the Mediterranean island of Corsica for the 2000–2018 period. These hindcast simulations are compared to their driving Regional Climate Model (RCM) CNRM-ALADIN (12.5-km horizontal resolution and parameterised convection), weather stations for precipitation and wind and gridded precipitation datasets. The main benefits are found in the representation of (i) precipitation extremes resulting mainly from mesoscale convective systems affected by steep mountains during autumn and (ii) the formation of convection through thermally induced diurnal circulations and their interaction with the orography during summer. Simulations of hourly precipitation extremes, the diurnal cycle of precipitation, the distribution of precipitation intensities, the duration of precipitation events, and sea breezes are all improved in the 2.5-km simulations with respect to the RCM, confirming an added value. However, existing differences between model simulations and observations are difficult to explain as the main biases are related to the availability and quality of observations, particularly at high elevations. Overall, better results from the 2.5-km resolution, increase our confidence in CP-RCMs to investigate future climate projections for Corsica and islands with complex terrain.

Abstract The Canadian Precipitation Analysis System (CaPA) is an operational system that uses a combination of weather gauge and ground-based radar measurements together with short-term forecasts from a numerical weather model to provide near-real-time estimates of 6- and 24-h precipitation amounts. During the winter season, many gauge measurements are rejected by the CaPA quality control process because of the wind-induced undercatch for solid precipitation. The goal of this study is to improve the precipitation estimates over central Canada during the winter seasons from 2019 to 2022. Two approaches were tested. First, the quality control procedure in CaPA has been relaxed to increase the number of surface observations assimilated. Second, the automatic solid precipitation measurements were adjusted using a universal transfer function to compensate for the undercatch problem. Although increasing the wind speed threshold resulted in lower amounts and worse biases in frequency, the overall precipitation estimates are improved as the equitable threat score is improved because of a substantial decrease in the false alarm ratio, which compensates the degradation of the probability of detection. The increase of solid precipitation amounts using a transfer function improves the biases in both frequency and amounts and the probability of detection for all precipitation thresholds. However, the false alarm ratio deteriorates for large thresholds. The statistics vary from year to year, but an overall improvement is demonstrated by increasing the number of stations and adjusting the solid precipitation amounts for wind speed undercatch.

Abstract This study aims to characterize the shapes and fall speeds of ice pellets formed in various atmospheric conditions and to investigate the possibility to use a laser-optical disdrometer to distinguish between ice pellets and other types of precipitation. To do so, four ice pellet events were documented using manual observations, macrophotography, and laser-optical disdrometer data. First, various ice pellet fall speeds and shapes, including spherical, bulged, fractured, and irregular particles, were associated with distinct atmospheric conditions. A higher fraction of bulged and fractured ice pellets was observed when solid precipitation was completely melted aloft while more irregular particles were observed during partial melting. These characteristics affected the diameter–fall speed relations measured. Second, the measurements of particles’ fall speed and diameter show that ice pellets could be differentiated from rain or freezing rain. Ice pellets larger than 1.5 mm tend to fall > 0.5 m s −1 slower than raindrops of the same size. In addition, the fall speed of a small fraction of ice pellets was < 2 m s −1 regardless of their size, as compared with a fall speed > 3 m s −1 for ice pellets with diameter > 1.5 mm. Video analysis suggests that these slower particles could be ice pellets passing through the laser-optical disdrometer after colliding with the head of the instrument. Overall, these findings contribute to a better understanding of the microphysics of ice pellets and their measurement using a laser-optical disdrometer. Significance Statement Ice pellets are challenging to forecast and to detect automatically. In this study, we documented the fall speed and physical characteristics of ice pellets during various atmospheric conditions using a combination of a laser-optical disdrometer, manual observations, and macrophotography images. Relationships were found between the shape and fall speed of ice pellets. These findings could be used to refine the parameterization of ice pellets in atmospheric models and, consequently, improve the forecast of impactful winter precipitation types such as freezing rain. Furthermore, they will also help to physically interpret laser-optical disdrometer data during ice pellets and freezing rain.

Abstract Since a decade, convection-permitting regional climate models (CPRCM) have emerged showing promising results, especially in improving the simulation of precipitation extremes. In this article, the CPRCM CNRM-AROME developed at the Centre National de Recherches Météorologiques (CNRM) since a few years is described and evaluated using a 2.5-km 19-year long hindcast simulation over a large northwestern European domain using different observations through an added-value analysis in which a comparison with its driving 12-km RCM CNRM-ALADIN is performed. The evaluation is challenging due to the lack of high-quality observations at both high temporal and spatial resolutions. Thus, a high spatio-temporal observed gridded precipitation dataset was built from the collection of seven national datasets that helped the identification of added value in CNRM-AROME. The evaluation is based on a series of standard climatic features that include long-term means and mean annual cycles of precipitation and near-surface temperature where CNRM-AROME shows little improvements compared to CNRM-ALADIN. Additional indicators such as the summer diurnal cycle and indices of extreme precipitation show, on the contrary, a more realistic behaviour of the CNRM-AROME model. Moreover, the analysis of snow cover shows a clear added-value in the CNRM-AROME simulation, principally due to the improved description of the orography with the CPRCM high resolution. Additional analyses include the evaluation of incoming shortwave radiation, and cloud cover using satellite estimates. Overall, despite some systematic biases, the evaluation indicates that CNRM-AROME is a suitable CPRCM that is superior in many aspects to the RCM CNRM-ALADIN.

Abstract Current‐generation climate models project that Africa will warm by up to 5°C in the coming century, severely stressing African populations. Past and ongoing work indicates, however, that the models used to create these projections do not match proxy records of past temperature in Africa during the mid‐Holocene (MH), raising concerns that their future projections may house large uncertainties. Rather than reproducing proxy‐based reconstructions of MH warming relative to the Pre‐Industrial (PI), models instead simulate MH temperatures very similar to or slightly colder than the PI. This data‐model mismatch could be due to a variety of factors, including biases in model surface energy budgets or inaccurate representation of the feedbacks between temperature and hydrologic change during the “Green Sahara.” We focus on the differences among model simulations in the Paleoclimate Modeling Intercomparison Project Phases 3 and 4 (PMIP3 and PMIP4), examining surface temperature and energy budgets to investigate controls on temperature and the potential model sources of this paleoclimate data‐model mismatch. Our results suggest that colder conditions simulated by PMIP3 and PMIP4 models during the MH are in large part due to the joint impacts of feedback uncertainties in response to increased precipitation, a strengthened West African Monsoon (WAM) in the Sahel, and the Green Sahara. We extend these insights into suggestions for model physics and boundary condition changes, and discuss implications for the accuracy of future climate model projections over Africa. , Key Points We evaluate the simulation of African air temperatures in Paleoclimate Modeling Intercomparison Project Phases 3 and 4 simulations of the mid‐Holocene Energy balance decomposition analyses indicate the hydrologic cycle plays a key role in causing mid‐Holocene cooling in model simulations “Green Sahara” experiments show that dust and vegetation affect simulated temperatures, revealing pathways for refining model simulations