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As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research. [ABSTRACT FROM AUTHOR]

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought-monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America and to enhance the utility of drought-monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impact information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and its indicators are changing over time as the climate warms and by recommending ways to strengthen the use of indices and indicators in drought decision making. [ABSTRACT FROM AUTHOR]

Climate change indices (CCI) profoundly contribute to understanding the climate and its change. They are used to present climate change in an easy-to-understand and tangible way, thus, facilitating climate communication. Most of these indices are calculated by daily data but there are also many valuable data sets that consist solely of a monthly temporal frequency. In this paper, we present a method that enables the estimation of specific CCIs from monthly instead of daily data, allowing the expression and examination of data sets consisting solely of monthly parameters through climate change indices. Therefore, we used the ERA5 Land data supplemented by a CMIP6 ssp5-8.5 climate projection to train multiple regression models with different regression functions and selected the best fitting for every grid point. Using a climate projection as a supplement in training the regression functions accounts for climate change and empowers the method's application in future climate periods. The method includes a simple bias adjustment (delta change). Its output is regridded to ERA5 Land's 0. 1 ∘ grid, adapting it to the local environment and offering better application in areas with complex terrain using coarse data. Furthermore, the presented method and its regression coefficients can be created from any data set, allowing an even higher spatial resolution than ERA5 Land's. While the method performs best for the temperature-related indices in warm temperate climates, it works uniformly well for the precipitation-related index maximum consecutive dry days on a global scale. [ABSTRACT FROM AUTHOR]

The rainfall distribution in southwest China is uneven, and the rainstorm threshold cannot use in a unified standard. This paper synthesizes a calculation method for the extremely heavy precipitation threshold and the provision of the rainstorm threshold in meteorological operation. It calculates the daily precipitation rainstorm threshold at 400 national ground stations in southwest China. The rainstorm events from 1961 to 2021 were statistically analyzed using the rainstorm threshold and analyzing the spatial-temporal variation characteristics. The results show that the number of single-station rainstorm events and the average precipitation of single-station rainstorm events in southwest China decreased from east to west. The number and frequency of single-station rainstorm events in Guizhou, Sichuan, Tibet, and Chongqing are increasing, while the number of single-station rainstorm events in Yunnan is decreasing. There is no apparent spatial distribution pattern for the continuous rainstorm events in the southwest region. From 1961 to 2021, the number and frequency of rainstorm events at a single station in southwest China followed an upward trend. The number of rainstorm events at a single station increased by 16.7 times · (10a)−1, and the frequency of rainstorms increased by 9.9% · (10a)−1. The continuous rainstorm events show an increasing trend, with an increase of 0.1 times · (10a)−1. Using the rainstorm threshold in southwest China, the early warning threshold for rainstorm disasters can be adjusted. The temporal and spatial characteristics of rainstorm events since 1961 can analyze the changes occurring in rainstorm events under global warming and provide data to support the response of southwest China to climate change. [ABSTRACT FROM AUTHOR]

Under global warming, the climate in semi-arid inland Eurasia (SAIE) has changed in an opposite manner, thereby seriously impacting the local ecological environment. However, the key influencing factors and physical mechanism remain inconclusive. In this paper, we remodel the precipitation recycling ratio (PRR) model to assess the contributions of moisture from different water vapor sources to local precipitation, analyze the characteristics of the PRR and precipitation in SAIE, and provide possible physical reasons based on the Clausius–Clapeyron equation. It is found that the PRR increased from 1970 to 2017 as the result of linear trend analysis, with obvious seasonality. Moreover, the component of precipitation contributed by locally evaporated moisture (Pl), and that contributed by advected moisture (Pa) as well as the total precipitation (P), increased during the past 48 years. In particular, the Pa, Pl, and P in autumn and winter all increased obviously during the past 20 years from the interdecadal change trend, as well as the PRR (Pl/P), which was opposite to the decrease in the total water vapor input I(Ω) in the horizontal direction. According to the Clausius–Clapeyron equation, one of the causes might be that global warming has accelerated the local water cycle and driven the increase in Pa, and the increase in atmospheric water holding capacity caused by global warming provides the power source. We suggest that the climate's transformation from dry to wet in SAIE can only be temporary since SAIE is an inland area and the adjustment of atmospheric circulation did not lead to the increase in external water vapor. [ABSTRACT FROM AUTHOR]

Due to global warming and other climate changes, it is increasingly important to study the response of regional environmental changes and dynamic changes in vegetation to climate change. Based on meteorological data from the last 60 years, this paper calculates the humidity index of western China under a wide range of long time series in different regions and explores the cross-correlation effect between series by offering a comparison with NDVI data, to analyze the cross-correlation between wet and dry changes and changes in vegetation in western China on a spatial scale. The results show that the spatial distribution of the interdecadal humidity index is different between different regions in western China. For example, the semi-arid and the semi-humid zones of the Weihe River region exhibit significant changes, while the Xinjiang and Qinghai–Tibet regions show a trend of constant wetness, on the whole, and the Sichuan and Yunnan–Guizhou regions are relatively humid and the distribution of wetness and dryness is relatively stable. The distribution of high and low values of the humidity index is very obvious and consistent with that of the distribution of desert bare land and precipitation in western China. In common with the distribution in the humidity index, the maximum correlation number between the NDVI and the humidity index in the whole western region is also significantly different in spatial distribution. There is a positive correlation between the NDVI and the humidity index in 99% of the study area. However, the delay in response time of the NDVI to changes in the humidity index in each region is inconsistent. For example, changes in the NDVI lag changes in the humidity index in the Menggan region by generally either 2 months or 5 months, while in the Sichuan region the delay in response time is generally 3 months. The variation and trend in dry and wet areas are closely related to the geographical location, climate zone, and topographic terrain, which may be the reason for the differences in the distribution of vegetation types and the response time to dry and wet changes. There is significant interaction between the humidity index and the vegetation type or precipitation distribution in western China. The positive correlation between the NDVI and the humidity index means that the positive effect is more sensitive, and the response of grassland is the most sensitive in the ecosystem. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP), which is well known as "The Third Pole", is of great importance to climate change in East Asia, and even the whole world. In this paper, we selected the monthly temperature (including the monthly mean and the maximum and minimum temperature) during 1971-2015 from 88 meteorological stations on the TP. The data were tested and corrected by using Penalized Maximal F Test (PMFT) based on RHtest. Afterwards, based on the Mann-Kendall test, we analyzed the seasonal and time-interval characteristics on each station in detail. The results show that the TP has experienced significant warming during 1971-2015. When comparing the selected elements, the warming rate of minimum temperature (Tmin) is the largest, the mean temperature (Tmean) comes second, and the maximum temperature (Tmax) is the smallest. The warming trends in four seasons are significant, and the highest warming rate occurs in winter. The warming trend on the TP has a prominent spatial difference, with a large warming rate on the eastern parts and a small one on the central regions. In different seasons, the warming trends on the TP have different characteristics in the time interval. Since 1998, the warming rate in spring increased markedly, spring has displaced winter as the season with the highest warming rate recently. [ABSTRACT FROM AUTHOR]

Temperature extremes can cause disastrous impacts on ecological and social economic systems. China is very sensitive to climate change, as its warming rate exceeds that of the global mean level. This paper focused on the spatial and temporal changes of the temperature extremes characterized by the 95th percentile of maximum temperature (TX95), the 5th percentile of the minimum temperature (TN5), high-temperature days (HTD) and low-temperature days (LTD). The daily maximum and minimum temperatures generated by PRECIS under different Representative Concentration Pathways (RCPs) are used in the research. The results show that: (1) Model simulation data can reproduce the spatial distribution features of the maximum temperature (Tmax) and minimum temperature (Tmin) as well as that of the extreme temperature indices; (2) By the end of the 21st century (2070-2099), both the Tmax and Tmin are warmer than the baseline level (1961-1990) in China and the eight sub-regions. However, there are regional differences in the asymmetrical warming features, as the Tmin warms more than the Tmax in the northern part of China and the Tibetan Plateau, while the Tmax warms more than the Tmin in the southern part of China; (3) The frequency of the warm extremes would become more usual, as the HTD characterized by the present-day threshold would increase by 106%, 196% and 346%, under RCP2.6, RCP4.5 and RCP8.5, respectively, while the cold extremes characterized by the LTD would become less frequent by the end of the 21st century, decreasing by 75%, 90% and 98% under RCP2.6, RCP4.5 and RCP8.5, respectively. The southern and eastern parts of the Tibetan Plateau respond sensitively to changes in both the hot and cold extremes, suggesting its higher likelihood to suffer from climate warming; (4) The intensity of the warm (cold) extremes would increase (decrease) significantly, characterized by the changes in the TX95 (TN5) by the end of the 21st century, and the magnitude of the increase in the TN5 is larger than that of the TX95 in the national mean value. The changes in both the TX95 and TN5 are larger under the higher versus weaker emission scenario, indicating that it is essential to reduce the GHG emissions and take adaptation measures in the future. [ABSTRACT FROM AUTHOR]

Some of the major dimensions of climate change include increase in surface temperature, longer spells of droughts in significant portions of the world, associated higher evapotranspiration rates, and so on. It is therefore essential to comprehend the future possible scenario of climate change in terms of global warming. A high resolution limited area Regional Climate Model (RCM) can produce reasonably appropriate projections to be used for climate-scenario generation in country-scale. This paper features the development of future surface temperature projections for Bangladesh on monthly resolution for each year from 2011 to 2100 applying Providing Regional Climates for Impacts Studies (PRECIS), and it explains in detail the modeling processes including the model features, domain size selection, bias identification as well as construction of change field for the concerned climatic variable, in this case, surface temperature. PRECIS was run on a 50 km horizontal grid-spacing under the Intergovernmental Panel on Climate Change (IPCC) A1B scenario and it was found to perform reasonably well in simulating future surface temperature of Bangladesh. The linear regression between observed and model simulated results of monthly average temperatures, within the 30-year period from 1971 to 2000, gives a high correlation of 0.93. The applied change field in average annual temperature shows only 0.5 °C-1 °C deviation from the observed values over the period from 2005 to 2008. Eventually, from the projected average temperature change during the years 1971--2000, it is apparent that warming in Bangladesh prevails invariably every month, which might eventually result in an average annual increase of 4 °C by the year 2100. Calculated anomalies in country-average annual temperature mostly remain on the positive side throughout the period of 2071--2100 indicating an overall up-shift. Apart from these quantitative analyses of temporal changes of temperature, this paper also illustrates their spatial distribution with a view to identify the most vulnerable zones under consequent warming through future times. [ABSTRACT FROM AUTHOR]

Emissions of fluorinated greenhouse gases (F-gases), which are used as replacements for ozone-depleting substances, have risen sharply since 1995. The rapid increase in F-gas emissions coupled with their global warming potential (GWP) has led to increased worldwide attention to monitoring emission levels and subsequently regulating the use of F-gases. These restrictions apply in particular to applications for which alternative technologies are available that are more economically efficient and have minor or no impact on the Earth's climate system. This paper brings new information about changes in composition of consumed F-gases in the Czech Republic. Since no F-gases are produced in the country, data about F-gas consumption are obtained from three resources which give information about import and export. The paper also describes implementation of newly used F-gases, which are used as replacements for specific F-gases, into emission calculation models. Emissions are estimated according to the methodology developed by the Intergovernmental Panel on Climate Change (IPCC). Although consumption of F-gases with high GWP has already started decreasing, it will have no effect on F-gas emissions for several years. [ABSTRACT FROM AUTHOR]

The warming observed over the past summers since 2000 is unprecedented in climate records in Europe and especially in France. Extreme temperatures and heat spells were often analyzed in the literature by applying extreme value theory but rarely in a non-stationary (NS) framework and duration modeling is often excluded. For a modern risk-based approach, it is important to have knowledge of the duration, magnitude, and frequency of occurrence of heat spells in a climate variability and change context. Yet, despite their obvious importance, teleconnections and associated climate indices (CIs) have often been excluded from heat spell modelling. The notion of duration is also not easily interpretable in a frequency analysis and can even be subtle, especially in a NS context. In this study, we used time-varying statistical distributions with parameters conditional on covariates representing the time and CIs. The daily maximum temperatures (DMTs) observed at the Orange and Dijon stations in France were used as a case study. This paper highlights a possible relationship between some large-scale climate patterns and the heat spells in France. Overall, the results suggest that considering the combined effect of global warming and these patterns in NS models is useful for a more appropriate characterization of the hazard heat spells in France. [ABSTRACT FROM AUTHOR]

Our understanding of the time variability of intense cyclones in the Mediterranean region is still lacking despite its importance for the long-term forecast of climate anomalies. This study examines the month-to-month variability and predictability of cyclones, the intensity of which exceeded the 75th percentile (intense cyclones) and the 95th percentile (extreme cyclones), over the Western and Eastern Mediterranean. The locations of cyclones were obtained by applying the method of M. Yu. Bardin on the 6-hourly 1000 hPa geopotential height data from the NCEP/NCAR reanalysis for the period 1951–2017 (67 years). It was shown that annual frequencies of cyclones were higher in the Western Mediterranean due to the contribution of spring and autumn; monthly averages were higher in the Eastern Mediterranean in December/January–March for intense/extreme cyclones. In the context of global warming, no linear trends significant at the 90% confidence level were found in the variability of intense and extreme cyclones, except for a positive trend in autumn extreme cyclones over the Eastern Mediterranean. The time series of cyclones in both parts of the Mediterranean were characterized by a pronounced interannual variability with a noticeable decadal modulation. According to spectral analysis, these interannual periods were multiples of 2–3 years corresponding to the main global teleconnection patterns. Seasonally, the most energy was concentrated in winter spectra; spring and autumn spectra had lower comparable magnitudes. The correlation analysis between the frequency of cyclones and the indices of the main atmospheric patterns showed that the main synchronous patterns for intense and extreme Mediterranean cyclones in September–April were the Mediterranean Oscillation (with the opposite signs for the Western and Eastern Mediterranean), Scandinavia pattern (positive correlation), and East Atlantic Oscillation (negative correlation). Additional important synchronous teleconnection patterns for some months were the Arctic Oscillation and East Atlantic/West Russia pattern for the Western Mediterranean, and the Polar/Eurasia pattern and Tropical Northern Hemisphere pattern for the Eastern Mediterranean. The outcome of this paper was the use of an artificial neural network model with inputs of global teleconnection indices both in the atmosphere and ocean to describe the temporal variability of the frequency of intense cyclones in the Western and Eastern Mediterranean. The predictability of intense cyclones was shown with the possibility of forecasts with a lead time of 0, 2, 4, and 6 months for the Western Mediterranean in October, January, February, April, and May, and for the Eastern Mediterranean in January, February, March, April, and May. One of the applications of this model may be in forecasting the evolution of the monthly frequency of cyclones with a lead time of 2 to 6 months. [ABSTRACT FROM AUTHOR]

The Earth has seen El Niño-Southern Oscillation (ENSO)—the leading mode of interannual climate variability—for at least millennia and likely over millions of years. This paper reviews previous studies from perspectives of both paleoclimate proxy data (from traditional sediment records to the latest high-resolution oxygen isotope records) and model simulations (including earlier intermediate models to the latest isotope-enabled coupled models). It summarizes current understanding of ENSO’s past evolution during both interglacial and glacial periods and its response to external climatic forcings such as volcanic, orbital, ice-sheet and greenhouse gas forcings. Due to the intrinsic irregularity of ENSO and its complicated relationship with other climate phenomena, reconstructions and model simulations of ENSO variability are subject to inherent difficulties in interpretations and biases. Resolving these challenges through new data syntheses, new statistical methods, more complex climate model simulations as well as direct model-data comparisons can potentially better constrain uncertainty regarding ENSO’s response to future global warming. [ABSTRACT FROM AUTHOR]