Analysing Historical and Modelling Future Soil Temperature at Kuujjuaq, Quebec (Canada): Implications on Aviation Infrastructure

The impact of climate change on soil temperatures at Kuujjuaq, Quebec in northern Canada is assessed. First, long-term historical soil temperature records (1967–1995) are statistically analyzed to provide a climatological baseline for soils at 5 to 150 cm depths. Next, the nature of the relationship between atmospheric variables and soil temperature are determined using a statistical downscaling model (SDSM) and National Centers for Environmental Prediction (NCEP), a climatological data set. SDSM was found to replicate historic soil temperatures well and used to project soil temperatures for the remainder of the century using climate model output Canadian Second Generation Earth System Model (CanESM2). Three Representative Concentration Pathway scenarios (RCP 2.6, 4.5 and 8.5) were used from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). This study found that the soil temperature at this location may warm at 0.9 to 1.2 °C per decade at various depths. Annual soil temperatures at all depths are projected to rise to above 0 °C for the 1997–2026 period for all climate scenarios. The melting soil poses a hazard to the airport infrastructure and will require adaptation measures.

GEFSv12 reforecast dataset for supporting subseasonal and hydrometeorological applications

For the newly implemented Global Ensemble Forecast System version 12 (GEFSv12), a 31-year (1989-2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System version 15.1 and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–1999) and Phase 2 (2000–2019) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast data set was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500hPa geopotential height, tropical storm track, precipitation, 2-meter temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.

About the possibility of using the output data of the global atmosphere model GFS NCEP in ecological research

Проведена оценка степени совпадения прогностических значений стратификации атмосферы с нарастающей заблаговременностью 24 часа, полученных из глобальной модели атмосферы GFS NCEP (Global Forecast System National Centers for Environmental Prediction) с фактическими данными аэрологического зондирования на основе корреляционного анализа. Актуальность работы заключается в том, что в настоящее время количество опасных природных явлений продолжает увеличиваться, в том числе и загрязнение атмосферы примесями, приводящими к глобальному потеплению. При прогнозировании опасных явлений для экологии входными данными являются значения полей метеопараметров по фактическим данным аэрологического зондирования атмосферы. Такие данные доступны только на отдельных метеостанциях, расположенных достаточно далеко друг от друга, что усложняет проведение исследований. Между тем инструменты для анализа и оценки распространения и рассеивания загрязняющих веществ в атмосфере в настоящее время получили значительное развитие. Сдерживающим фактором их более широкого применения заинтересованными структурами по прогнозированию качества воздуха, аварийно-спасательными службами, представителями авиации, государственными учреждениями и сообществом исследователей атмосферы является недостаток информации о текущем состоянии атмосферы, а также получение прогностических метеопараметров. Для решения этой проблемы предлагаются использовать данные глобальной модели атмосферы GFS NCEP. Целью исследования является определить правомерность замены фактических данных аэрологического зондирования атмосферы на прогностические поля стратифицированных метеопараметров из глобальной модели атмосферы. Методом исследования является один из методов статистического анализа данных - корреляционный анализ. В результате исследований получено, что коэффициенты корреляции между прогностическими и фактическими значениями температуры воздуха, температуры точки росы, скорости и направления ветра имеют высокие значения. Это делает возможными использование данных глобальной модели при математическом моделировании распространения загрязнения в атмосфере, а также прогнозе опасных стихийных явлений, таких как паводок, сильный ливень, град, сель, приводящих к нарушению природных экологических систем. The degree of matching of the predictive values of atmosphere stratification with an increasing lead time of 24 hours obtained from the global atmosphere model GFS NCEP (Global Forecast System National Centers for Environmental Prediction) and the actual data of aerological sounding based on correlation analysis was assessed. The relevance of the work lies in the fact that at present the number of natural hazards continues to increase, including atmospheric pollution with impurities leading to global warming. When predicting dangerous phenomena for the environment, the input data are the values of the fields of meteorological parameters based on the actual data of the aerological sounding of the atmosphere. Such data is available only at individual weather stations located far enough apart from each other, which complicates the research. Meanwhile, tools for analyzing and assessing the spread and dispersion of pollutants in the atmosphere have now received significant development. A limiting factor in their wider use by interested structures for predicting air quality, emergency services, aviation representatives, government agencies and the community of atmosphere researchers is the lack of information about the current state of the atmosphere, as well as obtaining predictive meteorological parameters. To solve this problem, data from the global atmosphere model GFS NCEP are proposed. The aim of the study is to determine the validity of replacing the actual data of the aerological sounding of the atmosphere with the predictive fields of stratified meteorological parameters from the global atmosphere model. The research method is correlation analysis, one of the methods of statistical data analysis. As a result of the research, it was found that the correlation coefficients between the predictive and actual values of air temperature, dew point temperature, wind speed and direction have high values. This makes it possible to use the data of the global model in mathematical modeling of atmospheric pollution, as well as the forecast of dangerous natural phenomena, such as floods, heavy rain, hail, mudslides, leading to disruption of natural ecological systems.

Simulation of air temperature and their influence on the potential distribution of Myracrodruon urundeuva, Copernicia prunifera and Cereus jamacaru in the Caatinga

Areas in the process of aridification in Caatinga phytogeographic domain in northeastern Brazil increase every year due to human intervention and increase in air temperature. The identification of long-term patterns and air temperature trends in the phytogeographic domain can express climate variability as well as a new phase of adaptation to some plant species. The temperature series from 1951 to 2018 obtained from the National Centers for Environmental Prediction data sets in four conservation areas with native vegetation, located in the North (A1), East (A2), South (A3) and West (A4) regions of this phytogeographic domain, show an increase in temperature between 0.5 and 1.4 °C over the 68-year period with the highest warming occurring in the months of March, April and May. The Maxent model is used to identify the influence of this increase on the presence potential of three species in the Caatinga, Myracrodruon urundeuva (aroeira), Copernicia prunifera (palmeira) and Cereus jamacaru DC (cactus) in the future time interval of 2041 to 2060, considering IPCC projected climate changes. The results show that climate change can lead to a reduction as well as redistribution of the potential areas of occurrence of the three species. Notable changes are: in the case of Carnauba, the high potential area reduces from 25.3% in the present state to 19.6% in 2050, and potential area for Aroeira diminishes in central Bahia and increases in Rio Grande do Norte. The projected changes for all three species are discussed.

A view of global monsoons in observed and model climatology

A 17 - year (1979-95) January and July climatology obtained from a T 62/ 28 -level version of the National Centers for Environmental Prediction (NCEP) global spectral operational model is compared with a mean observed climatology for the same period obtained from its reanalysis project, with a view to finding out how well it captures some of the well-thrown characteristics of the global monsoon circulation generated seasonally by differential heating of the earth's surface by the sun in the course of its annual oscillation about the equator. Good correspondence between the two is found in the fields of mean monthly anomaly (deviation of monthly mean from the annual mean) of surface temperature, surface pressure, atmospheric circulation and total rainfall over most parts of the globe, barring a few exceptions mostly in circulation and rainfall. Large diversity in the distribution and intensity of monsoon found over different regions due to land-sea configurations, cold and warm ocean surfaces and high mountain ranges appears to be well reflected in model and observed climatology. However, the concept of a single equatorial trough moving from one hemisphere to the other to cause advance and onset of monsoon appears to fail especially over warm oceans, where there appears to be evidence in favour of two troughs, one in each hemisphere. It is the equatorial trough in the summer hemisphere that moves to bring up the monsoon in that hemisphere. There appears to be some evidence to suggest an east-west movement of monsoons between major continents and oceans.

CLASI: coordinating innovative observations and modeling to improve coastal environmental prediction systems

The Coastal Land-Air-Sea-Interaction (CLASI) project aims to develop new “coast-aware” atmospheric boundary and surface layer parameterizations that represent the complex land-sea transition region through innovative observational and numerical modeling studies. The CLASI field effort will involve an extensive array of more than 40 land- and ocean-based moorings and towers deployed within varying coastal domains, including sandy, rocky, urban, and mountainous shorelines. Eight Air-Sea Interaction Spar (ASIS) buoys are positioned within the coastal and nearshore zone, the largest and most concentrated deployment of this unique, established measurement platform. Additionally, an array of novel nearshore buoys, and a network of land-based surface flux towers are complimented by spatial sampling from aircraft, shore-based radars, drones and satellites. CLASI also incorporates unique electromagnetic wave (EM) propagation measurements using coherent transmitter/receiver arrays to understand evaporation duct variability in the coastal zone. The goal of CLASI is to provide a rich dataset for validation of coupled, data assimilating large eddy simulations (LES) and the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®). CLASI observes four distinct coastal regimes within Monterey Bay, California (MB). By coordinating observations with COAMPS and LES simulations, the CLASI efforts will result in enhanced understanding of coastal physical processes and their representation in numerical weather prediction (NWP) models tailored to the coastal transition region. CLASI will also render a rich dataset for model evaluation and testing in support of future improvements to operational forecast models.

Variance of the Equatorial Atmospheric Circulations in the Reanalysis

The structure of the equatorial atmospheric circulation, as defined by the zonal mass streamfunction (ZMS), computed using the new fifth-generation ECMWF reanalysis for the global climate and weather (ERA-5) and the National Centers for Environmental Prediction NCEP–US Department of Energy reanalysis (NCEP-2) reanalysis products, is investigated and compared with Coupled Model Intercomparison Project Phase 6 (CMIP 6) ensemble mean. The equatorial atmospheric circulations majorly involve three components: the Indian Ocean cell (IOC), the Pacific Walker cell (POC) and the Atlantic Ocean cell (AOC). The IOC, POC and AOC average monthly or seasonal cycle peaks around March, June and February, respectively. ERA-5 has a higher IOC intensity from February to August, whereas NCEP-2 has a greater IOC intensity from September to December; NCEP-2 indicates greater POC intensity from January to May, whereas ERA-5 shows higher POC intensity from June to October. For the AOC, ERA-5 specifies greater intensity from March to August and NCEP-2 has a higher intensity from September to December. The equatorial atmospheric circulations cells vary in the reanalysis products, the IOC is weak and wider (weaker and smaller) in the ERA-5 (NCEP-2), the POC is more robust and wider (feebler and teensier) in NCEP-2 (ERA-5) and the AOC is weaker and wider (stronger and smaller) in ERA-5 (NCEP-2). ERA-5 revealed a farther westward POC and AOC compared to NCEP-2. In the CMIP 6 model ensemble mean (MME), the equatorial atmospheric circulations mean state indicated generally weaker cells, with the IOC smaller and the POC greater swinging eastward and westward, respectively, while the AOC is more westward. These changes in equatorial circulation correspond to changes in dynamically related heating in the tropics.

Distribution of coastal high water level during extreme events around the UK and Irish coasts

The interaction between waves, surges, and astronomical tides can lead to high coastal total water level (TWL), which can in turn trigger coastal flooding. Here, a high-resolution (1.5 km) simulation from a UK-focused regional coupled environmental prediction system is used to investigate the extreme events of winter 2013/4 around the UK and Irish coasts. The aim is to analyse the spatial distribution of coastal TWL and its components during this period by assessing (1) the relative contribution of different TWL components around the coast; (2) how extreme waves, surges, and tide interacted and if they occurred simultaneously; and (3) if this has implications in defining the severity of coastal hazard conditions. The TWL components' coastal distribution in winter 2013/4 was not constant in space, impacting differently over different regions. High (>90th percentile) waves and high surges occurred simultaneously at any tidal stage, including high tide (7.7 % of cases), but more often over the flood tide. During periods of high flood risk, a hazard proxy, defined as the sum of the sea surface height and half the significant wave height, at least doubled from average over three-quarters of the coast. These results have important implications for the risk management sector.

Wind Energy Potential Assessment Based-on WRF Four-Dimensional Data Assimilation System and Cross-Calibrated Multi-Platform Dataset

Indonesia has a target of achieving 23% of renewable energy share in the total energy mix in 2025. However, Indonesia does not have accurate and comprehensive data on renewable energy potentials, especially wind energy. This article aims to assess the theoretical potential of wind speed and to visualize the wind speed by province for the entire Indonesia. Our assessment relied on the Weather Research and Forecasting (WRF) model using Four-Dimensional Data Assimilation technique, also known as Nudging Newtonian relaxation. The robustness of our analysis is confirmed by using high-resolution data from the National Centers for Environmental Prediction–Final (NCEP - FNL) and Cross-Calibrated Multi-Platform (CCMP) Reanalysis satellite data. This study shows the WRF method is a feasible option to estimate wind speed data.