Climate, Volume 10, Issue 4 (April 2022) – 13 articles

In this study, we apply the ENVI-met model to evaluate the effects of combinations of morphological and vegetation-related landscape features on urban temperatures and thermal comfort. We simulated the thermal conditions of 126 scenarios, varying the aspect ratios of street canyons, vegetation cover and density, surface materials, and orientations toward the prevalent winds under an extreme heat situation. Our results show how the effects of physical and vegetation parameters interact and moderate each other. We also demonstrate how sensitive thermal comfort indices such as temperature and relative humidity are to the built environment parameters during different hours of a day. This study’s findings highlight the necessity of prioritizing heat mitigation interventions based on the site’s physical characteristics and landscape features and avoiding generic strategies for all types of urban environments. Full article

We examine North Atlantic climate variability using an ensemble of ocean reanalysis datasets to study the Atlantic Meridional Overturning Circulation (AMOC) from 1979 to 2018. The dataset intercomparison shows good agreement for the latest period (1995–2018) for AMOC dynamics, characterized by a weaker overturning circulation after 1995 and a more intense one during 1979–1995, with varying intensity across the various datasets. The correlation between leading empirical orthogonal functions suggests that the AMOC weakening has connections with cooler (warmer) sea surface temperature (SST) and lower (higher) ocean heat content in the subpolar (subtropical) gyre in the North Atlantic. Barotropic stream function and Gulf Stream index reveal a shrinking subpolar gyre and an expanding subtropical gyre during the strong-AMOC period and vice versa, consistently with Labrador Sea deep convection reduction. We also observed an east–west salt redistribution between the two periods. Additional analyses show that the AMOC variability is related to the North Atlantic Oscillation phase change around 1995. One of the datasets included in the comparison shows an overestimation of AMOC variability, notwithstanding the model SST bias reduction via ERA-Interim flux adjustments: further studies with a set of numerical experiments will help explain this behavior. Full article

As the effects of climate change accumulate and intensify, resource managers juggle existing goals and new mandates to operationalize adaptation. Fire managers contend with the direct effects of climate change on resources in addition to climate-induced disruptions to fire regimes and subsequent ecosystem effects. In systems stressed by warming and drying, increased fire activity amplifies the pace of change and scale of severe disturbance events, heightening the urgency for management action. Fire managers are asked to integrate information on climate impacts with their professional expertise to determine how to achieve management objectives in a changing climate with altered fire regimes. This is a difficult task, and managers need support as they incorporate climate adaptation into planning and operations. We present a list of adaptation strategies and approaches specific to fire and climate based on co-produced knowledge from a science–management partnership and pilot-tested in a two-day workshop with natural resource managers and regional stakeholders. This “menu” is a flexible and useful tool for fire managers who need to connect the dots between fire ecology, climate science, adaptation intent, and management implementation. It was created and tested as part of an adaptation framework used widely across the United States and should be applicable and useful in many fire-prone forest ecosystems. Full article

The conducted research is of particular importance for the country’s food security in the context of climate change in Southeastern Poland. The aim of the research was to determine the influence of climate on the variability of the appearance and the rate of spread of potato blights as the main factor limiting the potato yield in the conditions of Central and Eastern Europe. Combined statistical and simulation modeling methods were used. A mixed effect model was used to detect the effects of temperature, humidity, rainfall and wind speed on potato yield, and partial regression analysis models were used. The natural, agricultural and economic conditions in terms of suitability for potato cultivation were assessed, and factors influencing the fluctuation of the cultivated acreage, yield and harvesting of potatoes were identified. The forecast was based on empirical data from 2000 to 2019. It has been proven that potato cultivation in Southeastern Poland is more vulnerable to climate change than in the rest of the country. The results obtained from analyzing multi-annual results can help policymakers to develop strategies to increase the stability of future potato production and the safety of the crop. This will enable the better use of generated data and methodological approaches to analyze the role of climate, both on a regional and global scale. Full article

Several studies demonstrate the potential of models for the representation of phenomena such as urban heat islands. This article aimed to analyze atmospheric heat islands (UHI_ucl) by integrating primary air temperature data with spatial information such as land use and relief from a multicriteria model based on multiple linear regression. Furthermore, we compared the measured and estimated air temperature at 11 p.m. with the surface temperature at 10:51 p.m. (local time). These temperatures were obtained through the thermal band of the Landsat 8 satellite considering extraction points of interest in Presidente Prudente city, Brazil. The multicriteria model showed reliability in UHI_ucl spatialization, reaching the confidence interval (p-value ≤ 0.05). The model proves that urban surface materials are the main energy sources modulating heat transfer to the atmosphere, while vegetation has a temperature-reducing effect. Precise mappings such as the one proposed here are relevant for the formulation of measures that support decision-making by public authorities. These mappings aim at urban planning that is resilient to the effects of urban climate and can be replicated in other realities. Full article

The public housing stock across the European Union is generally constituted of old buildings (built prior to 1980) with high energy demand and indoor thermal comfort issues, which could be exacerbated by climate change. The aim of this paper was to quantify the impact of climate change on the energy demand of the public housing building stock. A neighbourhood located in Bari (south Italy) is considered as representative of a common construction typology of late 1970s in Italy. Energy models were created and calibrated with real-time data collected from utilities’ bills. The results showed a medium to strong correlation between age and energy consumption (r = 0.358), but no evident correlation between the number of tenants and energy consumption, although a significantly low energy consumption was found in apartments occupied by more than five tenants. An energy penalty of about 7 kWh/m² of heating energy consumption for every 10 years of increase in the average age of tenants was calculated. Moreover, the impact of future weather scenarios on energy consumptions was analysed and an average annual energy penalty of 0.3 kWh/m² was found. Full article

The jet stream over North America alternates between a more zonal direction and a wavy pattern (a more meridional flow) associated with persistent blocking patterns. To better understand these important patterns, we base our study on the frequency of winter (November–February) events during 1981–2020, based on four circulation regime types: blocking, the Alaskan Ridge, North American Ridge/Pacific Wave-Train; and zonal, the Pacific Trough and the central Pacific High/Arctic Low (Amini and Straus 2019). Increased information on within and between season variability is important, as the impacts of blocking include the California heatwave and mid-continent or east coast cold spells. Rather than extensive pattern duration or significant trends, temporal variability is the major feature. In some years the combination of the Alaskan Ridge and North American Ridge/Pacific Wave-Train patterns represent ~5 major events covering 35 days of the 120-day winter period, with individual events lasting 10 days. Within-season multiple occurrences and short durations dominate the winter meteorology of the continental United States. The characterization of the persistence of these blocking events is relevant for extended range forecasts. Full article

A detailed climatology of ocean wind waves in the South Atlantic Ocean, based on ERA-5 reanalysis and in a higher-resolution wave hindcast (ERA-5H), both developed by the European Centre for Medium-Range Weather Forecasts, is presented. The higher resolution of the wave fields in the ERA-5H (22 km) allowed for a better description of the wind sea and swell features compared to previous global and regional studies along the Brazilian coast. Overall, it is shown that swell waves are more prevalent and carry more energy in the offshore area of the study area, while wind sea waves dominate the nearshore regions, especially along the northern coast of Brazil. The influence of different climate indices on the significant wave heights patterns is also presented, with two behavioral groups showing opposite correlations to the North Atlantic Oscillation and Southern Annular Mode than to the Southern Oscillation Index. The analysis of the decadal trends of wind sea and swell heights during the ERA-5H period (1979–2020) shows that the long-term trends of the total significant wave height in the South Atlantic Ocean are mostly due to swell events and the wave propagation effect from Southern Ocean storms. Full article

Forest restoration is increasingly becoming a priority at international and national levels. Identifying forest degradation, however, is challenging because its drivers are underlying and site-specific. Existing frameworks and principles for identifying forest degradation are useful at larger scales, however, a framework that includes iterative input from local knowledge-holders would be useful at smaller scales. Here, we present a new mechanism; a framework for developing criteria and indicators that enables an approach for the identification of forest degradation and opportunities for restoration in landscapes that is free from failures that are often inherent to project cycles. The Degradation and Restoration Assessment Mechanism (DReAM) uses an iterative process that is based on local expertise and established regional knowledge to inform what is forest degradation and how to monitor restoration. We tested the mechanism’s utility at several sites in the Lancang-Mekong Region (Cambodia, Laos, Myanmar, Thailand, and Vietnam). The application of this mechanism rendered a suite of appropriate criteria and indicators for use in identifying degraded forests which can help inform detailed guidelines to develop rehabilitation approaches. The mechanism is designed to be utilized by any individual or group that is interested in degradation identification and/or rehabilitation assessment. Full article

This study is focused on the impact of different parameterizations in the state-of-the art wave model WAVEWATCH3 (WW3) in describing the present climate and future wave climate projections. We have used a Coupled Model Intercomparison Project Phase 6 (CMIP6)-derived single-wind forcing (from EC-EARTH) to produce a dynamic wind-wave climate ensemble for its historic (1995–2014) and future (2081–2100) periods. We discuss the uncertainty due to the wave model (intra-model uncertainty) in simulating the present and future wave climate. The historical wave climate runs were compared against the ERA5 reanalysis and found to be in good agreement for the significant wave height. This gives a good degree of confidence to investigate the intra-model uncertainty in WW3 using the available physics packages such as ST2, ST3, ST4, and ST6. In general, for the historic period, ST3 and ST4 physics packages perform better in the tropics whereas ST6 performs better in the extratropics, based on M-Score performance assessment. The study also reveals that the extratropical South Indian Ocean and tropical eastern South Pacific areas exhibit a larger amount of uncertainty, mainly induced by the ST2 physics package. The results of this study shed new light on the impacts associated with the use of multiple physics parameterizations in wave climate ensembles, an issue that has not received the necessary attention in scientific literature. Full article

The predicted climate change threatens food security in the coming years in Algeria. So, this study aims to assess the impact of future climate change on a key crop in Algeria which is rainfed durum wheat. We investigate the impact of climate change on rainfed durum wheat cultivar called Mexicali using AquaCrop crop model and the EURO-CORDEX climate projections downscaled with the ICHEC_KNMI model under RCP 4.5 and RCP 8.5. A delta method was applied to correct the incertitudes present in the raw climate projections of two experimental sites located in Sétif and Bordj Bou Arreridj (BBA)’s Eastern High plains of Algeria (EHPs). AquaCrop was validated with a good precision (RMSE = 0.41 tha⁻¹) to simulate Mexicali cultivar yields. In 2035–2064, it is expected at both sites: an average wheat grain yield enhances of +49% and +105% under RCP 4.5 and RCP 8.5, respectively, compared to the average yield of the baseline period (1981–2010), estimated at 29 qha⁻¹. In both sites, in 2035–2064, under RCP 4.5 and RCP 8.5, the CO₂ concentrations elevation has a fertilizing effect on rainfed wheat yield. This effect compensates for the negative impacts induced by the temperatures increase and decline in precipitation and net solar radiation. An increase in wheat water productivity is predicted under both RCPs scenarios. That is due to the water loss drop induced by the shortening of the wheat-growing cycle length by the effect of temperatures increase. In 2035–2064, early sowing in mid-September and October will lead to wheat yields improvement, as it will allow the wheat plant to benefit from the precipitations increase through the fall season. Thus, this early sowing will ensure a well vegetative development and will allow the wheat’s flowering and grain filling before the spring warming period. Full article

Wildfire is an important but understudied natural hazard in some areas. This research examined historical and future wildfire property risk at the census-block level in Louisiana, a U.S.A. state with relatively dense population and substantial vulnerability to loss from wildfire, despite its wet climate. Here wildfire risk is defined as the product of exposure and vulnerability to the hazard, where exposure is a function of the historical and anticipated future wildfire frequency/extent, and vulnerability is a function of population, structure and content property value, damage probability, and percent of properties damaged. The results revealed a historical (1992–2015) average annual statewide property loss due to wildfire of almost USD 5.6 million (in 2010 USD), with the greatest risk in southwestern inland, east-central, extreme northwestern, and coastal southwestern Louisiana. The geographic distribution of wildfire risk by 2050 will remain similar to that today, but the magnitude of losses was projected to increase statewide to over USD 11 million by 2050 (in 2010 USD), an increase of more than 100% over 2010 values. These estimates are conservative, as they did not include crop, forestry, or indirect losses (e.g., cost of evacuation and missed time at work). Overall, results suggested that increased efforts are needed to contain wildfires, to reduce the future risk of this increasing and underestimated hazard. Full article

Responding to infrastructural damage in the aftermath of natural disasters at a national, regional, and local level poses a significant challenge. Damage to road networks, clean water supply, and sanitation infrastructures, as well as social amenities like schools and hospitals, exacerbates the circumstances. As safe water sources are destroyed or mixed with contaminated water during a disaster, the risk of a waterborne disease outbreak is elevated in those disaster-affected locations. A country such as Haiti, where a large quantity of the population is deprived of safe water and basic sanitation facilities, would suffer more in post-disaster scenarios. Early warning of waterborne diseases like cholera would be of great help for humanitarian aid, and the management of disease outbreak perspectives. The challenging task in disease forecasting is to identify the suitable variables that would better predict a potential outbreak. In this study, we developed five (5) models including a machine learning approach, to identify and determine the impact of the environmental and social variables that play a significant role in post-disaster cholera outbreaks. We implemented the model setup with cholera outbreak data in Haiti after the landfall of Hurricane Matthew in October 2016. Our results demonstrate that adding high-resolution data in combination with appropriate social and environmental variables is helpful for better cholera forecasting in a post-disaster scenario. In addition, using a machine learning approach in combination with existing statistical or mechanistic models provides important insights into the selection of variables and identification of cholera risk hotspots, which can address the shortcomings of existing approaches. Full article