Development of 1961-1990 monthly surface climatology of India and patterns of differences of some meteorological parameters with respect to the 1951-1980 climatology

Climate normals are used to describe the average climatic conditions of a particular place and are computed by National Meteorological Services of all countries. The World Meteorological Organization (WMO) recommends that all countries prepare climate normals for the 30-year periods ending in 1930, 1960, 1990 and so on, for which the WMO World Climate Normals are published. Recently, Climatological Normals for the period 1961-1990 have been prepared by India Meteorological Department (IMD) which will change the baseline of comparison from 1951-1980. In this paper, preparation of the 30-year Climatological Normals of India for the period 1961 to 1990 and spatial patterns of differences of annual means of temperatures, relative humidity, clouds, rainfall and wind speed from the previous normals (1951-1980) are documented.The changes from earlier climatological normals indicate increase in annual means of maximum temperature, relative humidity and decrease in annual means of minimum temperature, cloud amount, rainfall, rainy days and wind speed over large parts of the country during 1961-1990. The spatial patterns of changes in dry bulb temperatures and relative humidity are complementary over most parts of the country. Compared with 1951-1980 climatology, there are large scale decreases in annual mean rainfall, rainy days and wind speed over most parts of the country during 1961-1990. The decrease in wind speed may be partly due to changes in exposure conditions of observatories due to urbanization.

Analysis of long-term climatic changes at Hodeidah-Yemen during the period between 1984 and 2019

Yemen is one of the Arab country that is vulnerable to climate changes, and this is clear from the indicators of impact on water resources, coastal zone environments, etc. This work focuses on studying the climatic variability at Hodeidah city-Yemen during the period between 1984 and 2019. This study aimed to characterize trends in mean monthly, seasonal and annual temperature. To attain these objectives the collected data were analyzed using both parametric (linear regression) and non-parametric (Mann–Kendall, Spearman and Sen's slope estimator tests) methods to detect the trend and the magnitudes of rates of changes of temperature over time. Analysis of data indicates clear climatic fluctuations of temperature. The annual means of temperature during the period of study were varied between 26.9°C and 30.1°C. The warmest years were observed during the more recent years of the study period ( 2005 to 2018). The increasing rate of annual temperature is about + 0.075°C /year, + 0.37°C/5year, + 0.75°C/decade ,+2.53°C, over the whole period of study(1985 to 2019), + 3.7°C/50 year and increase to + 4.85°C in 2050. On a monthly timescale, there are similar magnitudes of rates of change from December to September with highest rates in October and November. The results also showed that most months and seasons have significant positive trends in temperature and (Z-α/2) values of the MK Test > 1.96 and positive value of Sen’s slope estimator indicates significant an increasing trend towards warmer years. Anomalies of temperature confirm significant increasing trends towards warmer years (2000s to 2019).

Analysis of long-term climatic changes at Hodeidah-Yemen during the period between 1984 and 2019

Yemen is one of the Arab country that is vulnerable to climate changes, and this is clear from the indicators of impact on water resources, coastal zone environments, etc. This work focuses on studying the climatic variability at Hodeidah city-Yemen during the period between 1984 and 2019. This study aimed to characterize trends in mean monthly, seasonal and annual temperature. To attain these objectives the collected data were analyzed using both parametric (linear regression) and non-parametric (Mann–Kendall, Spearman and Sen's slope estimator tests) methods to detect the trend and the magnitudes of rates of changes of temperature over time. Analysis of data indicates clear climatic fluctuations of temperature. The annual means of temperature during the period of study were varied between 26.9°C and 30.1°C. The warmest years were observed during the more recent years of the study period ( 2005 to 2018). The increasing rate of annual temperature is about + 0.075°C /year, + 0.37°C/5year, + 0.75°C/decade ,+2.53°C, over the whole period of study(1985 to 2019), + 3.7°C/50 year and increase to + 4.85°C in 2050. On a monthly timescale, there are similar magnitudes of rates of change from December to September with highest rates in October and November. The results also showed that most months and seasons have significant positive trends in temperature and (Z-α/2) values of the MK Test > 1.96 and positive value of Sen’s slope estimator indicates significant an increasing trend towards warmer years. Anomalies of temperature confirm significant increasing trends towards warmer years (2000s to 2019).

Characteristics and Development Mechanisms of Northeast Cold Vortices

The northeast cold vortices (NECVs) in May-September during 1989–2018 are classified, based on the 6 h NCEP/NCAR reanalysis data (2.5° × 2.5°) and observational data from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) provided by China Meteorological Administration. Meanwhile, characteristics and development mechanisms for NECVs of different types are also analyzed. In the recent 30 years, the occurrences of NECV processes have been increasing year by year, with an average of 7.4 times per year in Northeast China and a duration of 3–5 days on average for each process. NECVs mostly occur in late spring and early summer, and the longest time influenced by NECVs exceeds 19 days, with annual means of 9.9 days, 8.8 days, and 7.0 days in May, June, and July, respectively. The frequency of weak NECVs is about 1.2 times that of strong NECVs. Strong NCVs in late spring and early autumn as well as weak MCVs in summer are with high-frequency occurrences. It is found that when NCVs occur in late spring and early autumn, the upper-level westerly jets are relatively stronger, thus strengthening the divergence in the upper troposphere and the vortex circulation. The circulation fields in upper and lower levels cooperate with the strong jets, promoting the continuous development and maintenance of the cold vortices. Apart from the jets and circulation, the lower central potential height combined with the obvious cold-core and stronger ascending motions favor the NCV’s development. In addition, the dry intrusion has a strong promotion due to the stronger lower-level cold advection and downward intrusion of high potential vorticity. However, when MCVs occur in summer, things are just the opposite.

IMPACT OF BUCHAREST WASTEWATER ON DAMBOVITA RIVER WATER QUALITY (2010-2015)

Bucharest, the capital of Romania, is located on the banks of Dambovita River, tributary of Arges River, which, in its turn, flows into the Danube, the second longest river in Europe. Until 2011, Bucharest wastewater treatment plant (WWTP) had no advanced treatment, and since the end of 2011 the plant is able to treat half of the incoming flow. The second upgrading phase is under construction. This paper presents monitoring data of Dambovita River, upstream and downstream from Bucharest WWTP, during the period 2010-2015. Annual means of main nutrients concentrations show that water quality was mostly in the first class before the WWTP, according to Romanian norms, and in the worst class downstream from the WWTP, particularly for ammonium and total phosphorus, which are indicators of sewerage pollution. Pollution is attenuated by dilution after confluence with the Arges River. Principal Component Analysis and factor analysis of monitoring data show the differences between sampling locations and strong positive correlations between ammonium, orthophosphates and total phosphorus. Nutrient pollution downstream from Bucharest has decreased after 2010, but more efforts to improve wastewater treatment are needed in order to comply with national and international regulations.

Intercomparison of Precipitation Estimates over the Southern Ocean from Atmospheric Reanalyses

Precipitation is a major component of the hydrologic cycle and plays a significant role in the sea ice mass balance in the polar regions. Over the Southern Ocean, precipitation is particularly uncertain due to the lack of direct observations in this remote and harsh environment. Here we demonstrate that precipitation estimates from eight global reanalyses produce similar spatial patterns between 2000 and 2010, although their annual means vary by about 250 mm yr−1 (or 26% of the median values) and there is little similarity in their representation of interannual variability. ERA-Interim produces the smallest and CFSR produces the largest amount of precipitation overall. Rainfall and snowfall are partitioned in five reanalyses; snowfall suffers from the same issues as the total precipitation comparison, with ERA-Interim producing about 128 mm less snowfall and JRA-55 about 103 mm more rainfall compared to the other reanalyses. When compared to CloudSat-derived snowfall, these five reanalyses indicate similar spatial patterns, but differ in their magnitude. All reanalyses indicate precipitation on nearly every day of the year, with spurious values occurring on an average of about 60 days yr−1, resulting in an accumulation of about 4.5 mm yr−1. While similarities in spatial patterns among the reanalyses suggest a convergence, the large spread in magnitudes points to issues with the background models in adequately reproducing precipitation rates, and the differences in the model physics employed. Further improvements to model physics are required to achieve confidence in precipitation rate, as well as the phase and frequency of precipitation in these products.

Description of the uEMEP_v5 downscaling approach for the EMEP MSC-W chemistry transport model

A description of the new air quality downscaling model – the urban EMEP (uEMEP) and its combination with the EMEP MSC-W model (European Monitoring and Evaluation Programme Meteorological Synthesising Centre West) – is presented. uEMEP is based on well-known Gaussian modelling principles. The uniqueness of the system is in its combination with the EMEP MSC-W model and the “local fraction” calculation contained within it. This allows the uEMEP model to be imbedded in the EMEP MSC-W model and downscaling can be carried out anywhere within the EMEP model domain, without any double counting of emissions, if appropriate proxy data are available that describe the spatial distribution of the emissions. This makes the model suitable for high-resolution calculations, down to 50 m, over entire countries. An example application, the Norwegian air quality forecasting and assessment system, is described where the entire country is modelled at a resolution of between 250 and 50 m. The model is validated against all available monitoring data, including traffic sites, in Norway. The results of the validation show good results for NO2, which has the best known emissions, and moderately good for PM10 and PM2.5. In Norway, the largest contributor to PM, even in cities, is long-range transport followed by road dust and domestic heating emissions. These contributors to PM are more difficult to quantify than NOx exhaust emission from traffic, which is the major contributor to NO2 concentrations. In addition to the validation results, a number of verification and sensitivity results are summarised. One verification showed that single annual mean calculations with a rotationally symmetric dispersion kernel give very similar results to the average of an entire year of hourly calculations, reducing the runtime for annual means by 4 orders of magnitude. The uEMEP model, in combination with EMEP MSC-W model, provides a new tool for assessing local-scale concentrations and exposure over large regions in a consistent and homogenous way and is suitable for large-scale policy applications.

Understanding the Distribution of Multimodel Ensembles

When analyzing multimodel climate ensembles it is often assumed that the ensemble is either truth centered or that models and observations are drawn from the same distribution. Here we analyze CMIP5 ensembles focusing on three measures that separate the two interpretations: the error of the ensemble mean relative to the error of individual models, the decay of the ensemble mean error for increasing ensemble size, and the correlations of the model errors. The measures are analyzed using a simple statistical model that includes the two interpretations as different limits and for which analytical results for the three measures can be obtained in high dimensions. We find that the simple statistical model describes the behavior of the three measures in the CMIP5 ensembles remarkably well. Except for the large-scale means we find that the indistinguishable interpretation is a better assumption than the truth centered interpretation. Furthermore, the indistinguishable interpretation becomes an increasingly better assumption when the errors are based on smaller temporal and spatial scales. Building on this, we present a simple conceptual mechanism for the indistinguishable interpretation based on the assumption that the climate models are calibrated on large-scale features such as, e.g., annual means or global averages.

Spatiotemporal Variability of Longshore Sediment Transport on the Brazilian Coast

The lack of planning and management regarding the transport of sediments along the coast can alter the existing equilibrium in coastal regions, causing or accelerating erosive processes and resulting in economic and environmental damage. Thus, it is important that the characteristics of the coastal drift be investigated, serving as source of information for future projects involving the coastal environment management. Within this context, the present work aims to identify the annual sediment transport averages along the Brazilian coast, the dominant direction of the coastal drift and its spatial variability. For this, a wind-generated wave modeling was considered, with 37 years of data (1979 to 2015), as well as four sediment transport formulations. For the spatiotemporal variability, the analysis of wavelets was applied, relating the effect of cycles of variability over the behavior of longshore sediment transport. The results showed that the largest annual volumes of sediment transport occurred in the northeastern Region of Brazil, reaching approximately 850000 m3/year-1. On the other hand, the smallest transport averages occurred in the southern region, in the state of Santa Catarina, reaching the value of 13497 m3/year-1. In the northeast region, annual and interannual cycles were more frequent and more energetic than in the southern Region, where short cycles presented similar importance to longer cycles, during 37 years of the study. However, in the overall context of the analysis, the long period cycles are more significant for longshore sediment transport, since this is a long term process. In this way, the present article contributes with information on longshore sediment transport, highlighting the annual means and the dominant drift, and deals with important questions about the influence of cycles of variability in the study region, emphasizing the importance of longer period events for the control of sedimentary transport in the Brazilian coast.

SYNERGISTIC USE OF SENTINEL-1 AND SENTINEL-2 TIME SERIES FOR POPLAR PLANTATIONS MONITORING AT LARGE SCALE

The current context of availability of Earth Observation satellite data at high spatial and temporal resolutions makes it possible to map large areas. Although supervised classification is the most widely adopted approach, its performance is highly dependent on the availability and the quality of training data. However, gathering samples from field surveys or through photo interpretation is often expensive and time-consuming especially when the area to be classified is large. In this paper we propose the use of an active learning-based technique to address this issue by reducing the labelling effort required for supervised classification while increasing the generalisation capabilities of the classifier across space. Experiments were conducted to identify poplar plantations in three different sites in France using Sentinel-2 time series. In order to characterise the age of the identified poplar stands, temporal means of Sentinel-1 backscatter coefficients were computed. The results are promising and show the good capacities of the active learning-based approach to achieve similar performance (Poplar F-score ≥ 90%) to traditional passive learning (i.e. with random selection of samples) with up to 50% fewer training samples. Sentinel-1 annual means have demonstrated their potential to differentiate two stand ages with an overall accuracy of 83% regardless of the cultivar considered.