SummaryThe Northern Bald Ibis (NBI) Geronticus eremita, is an ‘Endangered’ bird species of which only very few wild breeding colonies have survived along the Atlantic coast of south-west Morocco. This paper analyses ecological conditions of the 72 breeding sites of the NBI that have been known since 1900 in Morocco. Characterisation of breeding sites is based on physical criteria (elevation above sea level, geomorphology, mean annual precipitation and types of landscape) as well as land use, vegetation cover, infrastructure and types of settlement within three perimeters (0–1 km, > 1–5 km and > 5–10(20) km) using Google Earth satellite images. Statistical analyses of the number of breeding pairs, fledglings and rainfall during different quarters of the year from 1994 to 2016 in the two remaining breeding sites in Souss-Massa National Park and Tamri showed expected patterns as well as unexpected differences between the two localities. Based on our findings and indications in the literature, we suggest general and specific recommendations for potential future translocation projects of the NBI. Based on the analysis of the 28 breeding colonies existing after 1977, two elements emerge as the most important prerequisites: a low level of disturbances at the breeding sites and adequate feeding areas at a reasonable distance of 5–15 km.
Our world is rapidly changing. Societies are facing an increase in the frequency and intensity of high impact and extreme weather and climate events. These extremes together with exponential population growth and demographic shifts (e.g., urbanization, increase in coastal populations) are increasing the detrimental societal and economic impact of hazardous weather and climate events. Urbanization and our changing global economy have also increased the need for accurate projections of climate change and improved predictions of disruptive and potentially beneficial weather events on km-scales. Technological innovations are also leading to an evolving and growing role of the private sector in the weather and climate enterprise. This article discusses the challenges faced in accelerating advances in weather and climate forecasting and proposes a vision for key actions needed across the private, public, and academic sectors. Actions span: i) Utilizing the new observational and computing ecosystems; ii) Strategies to advance earth system models; iii) Ways to benefit from the growing role of artificial intelligence; iv) Practices to improve the communication of forecast information and decision support in our age of internet and social media; and v) Addressing the need to reduce the relatively large, detrimental impacts of weather and climate on all nations and especially on low income nations. These actions will be based on a model of improved cooperation between the public, private, and academic sectors. This article represents a concise summary of the White Paper on the Future of Weather and Climate Forecasting (2021) put together by the World Meteorological Organizations’s Open Consultative Platform.
<p>Grey infrastructures like buildings, roads and parking lots relate to surface sealing, lack of ventilation and anthropogenic heat &#8211; leading to effects like urban heat (UHI) and dry islands (= higher temperatures and lower relative humidity), and impact surface runoff during precipitation events. Hence, urban climate conditions differ significantly from their rural surroundings, demanding more granular data to quantify the effect city space has on weather parameters. However, observations and weather forecasts are usually made for rural areas, representative for a larger area &#8211; not for spaces where most people live, work and sleep. While a lot of data indeed exist for urban areas already &#8211; e.g., from satellites, radar stations and climate models &#8211; they all need calibration from measurements, in the city itself.</p> <p>Tallinn is the European Green Capital 2023. While it strives making green spaces more accessible for its people, grey infrastructure development is continuing and even expanding, sustaining and increasing the city&#8217;s urban heat and dry islands. On the other hand, Tallinn is conserving and investing in green infrastructure, like turning an old railway track into a green corridor and publicly open space (the so-called &#8220;Pollinator Highway&#8221;). This corridor connects living quarters of Tallinn`s outskirts with central areas and supports social inclusion by passing through diverse socio-economic districts.</p> <p>We created a concept to show the value of this green corridor for urban climate conditions. In May 2022, SEI Tallinn set up a network of 18 weather sensors measuring temperature, relative humidity and precipitation. The majority of stations are placed in green (5), urban green (5) and urban grey (5) spaces in the vicinity of the &#8220;Pollinator Highway&#8221;, with two more nearby the sea (to quantify land-sea-wind effects) and one near the official weather station. Data are open access, and live measurements publicly accessible.</p> <p>In this contribution, we evaluate the results of 10 months of measurements, with a spatiotemporal focus on how and where Tallinn&#8217;s UHI enhances the impacts of heat and mitigates cold waves. With the data presented in this contribution, we make urban climate challenges visible and climate communication more relevant to people, show the climatic value of green compared to grey city spaces (especially during heat waves) to municipal decision-makers and Tallinn&#8217;s citizens, determine the effect of the sea on Tallinn's climate and how it shapes Tallinn`s UHI, and finally support climate resilience and tailored adaptation solutions.</p>
<p>Cities worldwide will be affected by anthropogenic climate change and additionally cope with additional heat due to greater heat storage capacity of artificial surfaces, less ventilation, and a higher risk of extreme floods due to sealed surfaces. Urban heat island mitigation strategies (such as rooftop greening, increasing surface albedo of the city and irrigation of green surfaces, which leads to significant evaporative cooling) are well known, but the magnitude in air temperature reduction is still not fully understood and significantly differs between cities.</p><p>A small area (&#8220;Triangel area&#8221;) was desealed and planted with 18 young trees at a 1 ha area in the center of the Swiss City Basel in March 2021. This urban heat island mitigation strategy was validated with a high dense low-cost IoT measurement network, which was installed in Basel in 2020 to detect urban heat islands. To validate the mitigation strategy properly, 3 air temperature measurements were installed in the Triangel area in 2020 and compared with more than 20 air temperature measurements in the reference area outside the mitigated area.</p><p>The measurements showed that the Triangel area is in general around 0.2 K cooler than the reference area. Furthermore, the differences in air temperatures between Triangel and reference area were calculated before and after the mitigation action to test the effectiveness of the method. An air temperature reduction of the Triangel area of 0.4 K after the mitigation action (in comparison with the reference area) was observed by the measurements.</p><p>The results from the measurement campaign were compared with model results using the surface energy budget model SUEWS. The SUEWS model results confirm an air temperature decrease of 0.4 K for the chosen urban heat island mitigation strategy and suggest other mitigation strategies (such as rooftop greening, watering and more). This approach allows to estimate the best possible mitigation strategy for the Triangel area with the largest reduction of surface and air temperatures. In summary, the approach helps city councils taking the right decision by choosing the optimal cost-value ratio in urban heat island mitigation strategies and prevents costly (and non-climate effective) strategies.</p>
<p>The growth of urban areas in combination with an increased number of heatwaves worldwide caused by the anthropogenic climate change can make cities more vulnerable. Increasing number of buildings and sealed surfaces are changing the energy budget in urban areas towards higher longwave radiation fluxes due to the greater heat storage capacity.</p><p>Since WMO stations are typically located outside the city, where air temperatures normally are lower than in the city center, initial conditions of NWP models do not accurately represent the air temperatures in urban areas. Hence, NWP models tend to underestimate the air temperature in urban areas since NWP models cannot fully resolve the urban heat island effect. Without any post-processing the MAE is 1.7 K and the MBE is -1 K. This study focuses on an analysis of 17 different European cities in the year 2020. It quantifies the improvement of the statistical downscaling model over an NWP model by a) including dense air temperature measurements in the urban and rural areas, b) including satellite derived variables as model input and c) including both dense air temperature measurements and satellite derived variables.</p><p>Dense air temperature networks in cities help to better understand the micro-scale air temperature field in an urban environment. These air temperature data train a statistical high-resolution air temperature downscaling model for urban environments in 10 m horizontal resolution. Including official measurement stations, the statistical model can be transferred to other cities for an operational use to calculate micro-scale air temperatures on an hourly basis. The model is further forced by surface texture parameters from the high-resolution satellites Sentinel-2 and Landsat-8, as well as digital elevation models, and raw model output from meso-scale NWP models.</p><p>With a dense air temperature network (a), the urban heat island effect can be resolved, resulting in a reduced bias to almost 0 K. Including satellite derived variables as model input (b) the downscaling approach ensures to decrease the MAE by 0.4 K and to better represent the inner-city temperature variability. To better take dynamic processes into account, the downscaling approach can be extended with a dense measurement network (c) which also further reduces the MAE.</p><p>The statistical model approach enables to resolve high-resolution temperature fields in the past, making it possible to calculate high-resolution urban heat island maps. Furthermore, real-time temperature fields can help to significantly enhance the initial conditions for NWP models, thus improving forecast models in urban areas. A statistical downscaling of the numerical weather forecast can help decision-makers improving the heat wave management in cities.</p>
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