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The duration of land transfers plays a pivotal role in attaining agricultural sustainability and enhancing farmer’s welfare. Using household tracking survey data from China, this study investigates how climate anomalies affect the duration of land transfers. We find that abnormally high temperatures during the growing season shorten the duration of land rented in, while abnormally high precipitation during the growing season extends the duration of land rented out. Land rent is the main channel through which climate anomalies affect the duration of land transfers. Abnormally high precipitation reduces the unit rent of land rented out, while abnormally high temperatures increase the unit rent of land rented in. Additionally, household heads with a risk-lover attitude weakened the positive effect of abnormal high precipitation on the duration of land rented out, while larger areas of land rented out strengthened such effect. The negative effect of abnormally high temperatures on the duration of land rented in is more pronounced in hot regions. These findings offer valuable references for relevant government agencies to formulate policies to extend the duration of land transfers and safeguard farmer welfare amidst the challenges of climate change. [ABSTRACT FROM AUTHOR]

Over a decade (2013 to 2022), we investigated the variability of benthic macrofauna in two distinct environments characterized by contrasting structural complexities: salt marshes and adjacent unvegetated areas. We also evaluate the influence of eight teleconnection patterns in environmental variables in southern Brazil. We collected 58,818 organisms, distributed in 52 morphospecies, with an overlap of 63.5% of species between habitats. Our results indicate that positive periods of teleconnection patterns, such as Oceanic Niño Index (ONI) and the Pacific Decadal Oscillation (PDO), significantly impacted macrofauna density and structure, especially in unvegetated habitats where density reached peaks of 567 inds/0.03m2 during the El Niño events (2015–2016). Two opportunistic species, Monokallipseudes schubarti and Laeonereis acuta, represented more than 50% of all macrofauna and were responsible for density peaks over habitats in periods of positive anomalies (summer, 9.822 inds/m2; winter, 3.017 inds/m2). In general, unvegetated habitats exhibited higher macrofauna density, while salt marshes showed greater interannual homogeneity. As a result, we observed that salt marshes provided more beneficial conditions for macrofauna, maintaining consistent community structures, that reinforce the critical role of El Niño events in modulating benthic assemblages and the importance of salt marshes as resilient habitats in the face of climate anomalies. [ABSTRACT FROM AUTHOR]

New Zealand's National Institute of Water and Atmospheric Research publishes climate normals for New Zealand that are used for reporting the regional state of the climate, climate extremes and variability. Temperature and precipitation patterns are affected by both anthropogenic climate change and natural climate variability which in turn affect the climatological normal values calculated every decade. This study investigates how New Zealand's normals for temperature and precipitation have shifted over time at the national, regional and seasonal scales from the 1941–1970 period to the 1991–2020 period. Contrary to WMO recommendations, but aligned with many other countries, New Zealand's climate normals have traditionally not undergone homogenisation. The impact of introducing some homogenisation in the latest 1991–2020 normals, in contrast to historical non‐homogenized station normals, has been assessed using a new homogenized "Seventeen‐Station" temperature series from 1941 to 2020. We demonstrate that interpolating sparse non‐homogenized normals spatially to a grid can produce significant erroneous patterns and therefore undermine the accuracy of the conclusions drawn when using such normals. We find that the historical non‐homogenized temperature normals have a consistent negative bias in the long‐term trend at national, regional and seasonal scales. Our analysis of homogeneity tested precipitation showed consistent decreases at a national scale across all normal periods relative to the 1951–1980 precipitation normal. We also highlight how fixed period temperature and precipitation normals do not fully reflect the current state of a climate that is influenced by decadal variability and global warming. To derive normals fit for use in a changing climate it is suggested that automated methods for broad data homogenisation be developed along with alternative methods to derive normals that account for a non‐stationary climate. [ABSTRACT FROM AUTHOR]

Drought mechanisms vary markedly within different ecogeographical regions. Existing drought indices do not reflect the impact of climate anomalies on drought. In this study, the climate anomaly index was incorporated into the drought model based on the study of the effects of the El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) on drought in different eco-geographic zones of China. The model uses climate anomaly indices, meteorological drought indices, vegetation growth condition data, surface temperature, and biophysical attributes as characteristic variables and the Palmer Drought Severity Index (PDSI) as the dependent variable for model construction based on the Random Forest (RF) method. The results showed that the model has high accuracy for drought monitoring. The correlation coefficients between model results and observed drought condition values for all four seasons were above 0.95. The model was applied to drought monitoring in North China and the Huang-Huai-Hai region from 2006 to 2018. The statistical interpolation results of the meteorological drought indices and precipitation data were used to verify the application effect of the model. It was found that the model can accurately monitor drought caused by precipitation scarcity and reflect local variations in drought. This study provides a new model (Climate Anomaly Considering Integrated Surface Drought Index, CAC-ISDI) for drought monitoring that aims at quantitative and detailed monitoring of drought conditions and regional differences. It provides a robust method for accurate drought monitoring and evaluation in China and the rest of the world. [ABSTRACT FROM AUTHOR]

Canopy greenness is an indicator of ecosystem primary productivity, which is often limited by temperature and precipitation. Changes in vegetation greening have been reported mostly at global scales. However, we still have a poor understanding of vegetation greening patterns and drivers for major vegetation types, such as the tropical dry forest, one of the most extensive vegetation types in Mexico. Here, we analyze two decades of interannual variation in greenness and its relationship to temperature and precipitation in the northmost neotropical dry forest occurring in Northwestern Mexico. We constructed time-series linear regression models using standardized anomalies with z-scores (i.e., standard deviations away from the long-term mean) for the Normalized Difference Vegetation Index (NDVI) and the climate data from 2001 to 2021. Our best models indicate both temperature and precipitation exert positive effects on vegetation greenness, particularly from a lagged effect perspective, as retained predictors were the accumulated precipitation of two monsoon (summer) seasons and previous year mean temperature. The lowest levels of landscape greenness seem connected to prolonged droughts and extreme frost events. In fact, a switch from negative to positive NDVI anomalies was observed in the years following the February 2011 extreme frost that affected much of North America, including northern Mexico. Notable, under the stricter statistical criterion of −1.7 ≥ z score ≥ 1.7, only climatic variables presented very-extreme anomalies and these were not necessarily linked to a very-extreme response in landscape greenness. However, considering the criterion of −1.3 ≥ z score ≥ 1.3, we identified several extreme NDVI anomalies and their corresponding climatic anomaly. Therefore, more flexible statistical criteria might reveal climate extremes of ecological and social relevance. Our overall findings have implications for forest and climate risk management, particularly as extreme climatic anomalies are expected to continue increasing in light of climate change.

Offspring size is a key functional trait that can affect subsequent life history stages; in many species, it exhibits both local adaptation and phenotypic plasticity. Variation among populations in offspring size may be explained by various factors, including local climatic conditions. However, geographic variation in climate may be partitioned into long-term and interannual sources of variation, which may differ in their effects on population mean offspring size. To assess environmental correlates of offspring size, we evaluated geographic variation in seed mass among 88 populations representing 6 species of Streptanthus (Brassicaceae) distributed across a broad climatic gradient in California. We examined the effects of temperature-mediated growing season length and precipitation on population mean seed mass to determine whether it is best explained by (1) long-term mean climatic conditions; (2) interannual climate anomalies (i.e., deviations in climate from long-term means) during the year of seed development, or (3) interactions between climate variables. Both long-term mean climate and climate anomalies in the year of collection were associated with population mean seed mass, but their effects differed in direction and magnitude. Relatively large seeds were produced at chronically wet sites but also during drier-than-average years. This contrast indicates that these associations may be generated by different mechanisms (i.e., adaptive evolution vs. phenotypic plasticity) and may be evidence of countergradient plasticity in seed mass. In addition, populations occurring in locations characterized by relatively long growing seasons produced comparatively large seeds, particularly among chronically dry sites. This study highlights the need to consider that the responses of seed mass to long-term versus recent climatic conditions may differ and that climate variables may interact to predict seed mass. Such considerations are especially important when using these patterns to forecast the long- and short-term responses of seed mass to climate change. The results presented here also contribute to our broader understanding of how climate drives long-term (e.g., local adaptation) and short-term (e.g., phenotypic plasticity) variation in functional traits, such as offspring size across landscapes.

Climate scientists preferably use the term climate anomaly to explain any significant abnormal change of surface air temperature or precipitation in the weather pattern of any region. As a result, abnormal heat waves and extremely hot weather, severe winter snowfall and blizzards, long droughts, heavy rainfall, and extraordinary floods occur. Such anomalies were more often observed in different regions of the world during the last three decades. The term climate change, led by global warming due to the rising amount of carbon dioxide from burning fossil fuels, is generally considered the main cause. The ENSO phenomenon is also linked to the climate anomalies of the last three decades. In 2022 Europe, the USA, China, and Pakistan were affected by such climate anomalies. The Yangtze River region of China, the main rivers of European land, and the lakes and rivers of the dry land region of the USA experienced the worst long drought, creating an extreme shortage of water and electricity generation. On the other hand, Germany and southern China experienced the worst floods. Abnormally hot weather in the summer and extreme winter snowfall and blizzards severely affected people in Europe and the USA. In 2022, Pakistan was also severely affected by floods caused by abnormally heavy monsoon rainfall. Out of 170 districts, 38 were worst affected, causing the deaths of 1739 people, while houses and croplands of 33 million people were badly affected. Sindh was the most affected province, where heavy rainfall and poor drainage created catastrophic damage. Balochistan, southern Punjab, and districts of the Malakand region were severely affected by flash floods. The present study is based on the hypotheses that climatic anomalies (abnormal excessive rainfall and melting of snow) were the main causes of the flood disaster of 2022 in Pakistan. Failure of the National Disaster Management Authority is the secondary factor causing economic and human loss. [ABSTRACT FROM AUTHOR]

The World Meteorological Organization (WMO) recommends that the most recent 30-year period, i.e., 1991–2020, be used to compute the climate normals of geophysical variables. A unique aspect of this recent 30-year period is that the satellite-based observations of many different essential climate variables are available during this period, thus opening up new possibilities to provide a robust, global basis for the 30-year reference period in order to allow climate-monitoring and climate change studies. Here, using the satellite-based climate data record of cloud and radiation properties, CLARA-A3, for the month of January between 1981 and 2020, we illustrate the difference between the climate normal, as defined by guidelines from WMO on calculations of 30 yr climate normals, and climatology. It is shown that this difference is strongly dependent on the climate variable in question. We discuss the impacts of the nature and availability of satellite observations, variable definition, retrieval algorithm and programmatic configuration. It is shown that the satellite-based climate data records show enormous promise in providing a climate normal for the recent 30-year period (1991–2020) globally. We finally argue that the holistic perspectives from the global satellite community should be increasingly considered while formulating the future WMO guidelines on computing climate normals. [ABSTRACT FROM AUTHOR]

Climate variability is recognized as one of the greatest challenges of our world today and is predicted to have further adverse impacts on coffee production. Specific to Africa, Ethiopia is the leading coffee producer on the continent and is ranked 10th in global coffee export rankings; its coffee production is extremely sensitive to climate change, threatening the country's economy. This study was designed to assess the impact of climatic change and variability on smallholder farmers' coffee production in the Mattu district in southwest Ethiopia. Relevant secondary data from the Ethiopian national meteorology agency and the district agricultural office. The study shows the mean annual temperature in the district as 19.67°C over the past 32 years (1990-2022), and an increasing upward trend by a factor of 0.014°C per year and 0.14°C per decade. The mean total rainfall in the Mattu district over the same 32-year period was 1,408.6 mm, and the mean annual rainfall was 117.38 mm. The rainfall temporal pattern shows a decreasing trend over this period by a factor of 24.23 mm annually and 242.3 mm per decade. The district coffee production data confirm that temperature variability significantly affects coffee production at P<0.05. Given these trends, all stakeholders should further build and empower the ongoing community traditional and conventional natural resources management, and the government and related NGOs need to initiate policies, projects, strategies (including transformative new adaptation and mitigation strategies), and incentives that can assist smallholder farmers in sustainable coffee production. [ABSTRACT FROM AUTHOR]

The heat source of the Qinghai-Xizang Plateau not only strengthens the formation and maintenance of the climate background in drylands of China， but also has a significant impact on the climate anomalies there.A deep understanding of the relationship between Qinghai-Xizang Plateau heat source variety and climate anomalies in drylands of China is of great significance for maintaining ecological security， preventing major meteorological disasters and promoting regional sustainable development.This paper reviews and combs the current relevant research on the impact of Qinghai-Xizang Plateau heat sources on climate anomalies in drylands of China， including the climate characteristics of Qinghai-Xizang Plateau heat sources， the impact of Qinghai-Xizang Plateau heat sources on climate anomalies in drylands of China， and the prediction function of Qinghai-Xizang Plateau heat sources’ early signals on climate anomalies in drylands of China.Previous studies have shown that the temporal and spatial distribution characteristics of Qinghai-Xizang Plateau heat sources have significant discrepancy of seasonality， regionality and time stages.By regulating the circulation systems such as the meridional circulation on the northern side of Qinghai-Xizang Plateau， South Asian high， westerly jet and zonal planetary wave， it has an impact on the climate anomalies in drylands of China； At the same time， the Qinghai-Xizang Plateau snow and frozen soil change the surface energy balance and soil water and heat transfer through the land surface process， and affect the Qinghai-Xizang Plateau heat source， which can be used as the early signal for the prediction of summer precipitation in drylands of China.Finally， based on the summarizing of previous research progress and achievements， the future research direction of the impact of Qinghai-Xizang Plateau heat sources on climate anomalies in drylands of China is prospected.Under the background of global warming， there is still great uncertainty in the temporal and spatial variation law of Qinghai-Xizang Plateau heat sources.At present， the way of Qinghai-Xizang Plateau heat source affecting climate anomalies in drylands of China is not comprehensive， and the internal influence mechanism between them still needs to be further studied.

Abstrak. ENSO (El Niño - Southern Oscillation) adalah bentuk anomali iklim di Samudra Pasifik yang ditandai dengan peningkatan suhu permukaan laut (SPL) di bagian Tengah dan Timur khatulistiwa. Fenomena ini memainkan peran penting dalam variasi iklim tahunan dan musiman di Indonesia, terutama di wilayah khatulistiwa Indonesia. Studi ini bertujuan untuk menganalisis dampak anomali iklim komputasi di wilayah khatulistiwa Indonesia. Metode yang digunakan dalam studi ini adalah metode regresi linier dengan membandingkan dua mesin statistik, yaitu bahasa pemrograman Python dan SPSS. Pengaruh ENSO dirasakan di beberapa daerah Indonesia yang ditandai dengan jumlah curah hujan yang lebih rendah selama tahun ENSO dibandingkan dengan sebelum dan sesudah ENSO. Peristiwa El Niño juga mempengaruhi masuknya musim kemarau dan durasinya sepanjang evolusi UNSO. Penurunan jumlah curah hujan berkorelasi negatif dengan peningkatan jumlah kebakaran hutan per tahun. Analisis curah hujan berbasis pembelajaran mesin menggunakan Google Colab dan Python memberikan hasil yang identik dengan analisis berbasis SPSS, sehingga hasil analisis berbasis pembelajaran mesin memiliki nilai yang akurat. Perubahan iklim akan menghasilkan perubahan pola iklim tahunan dan antartahunan seperti penundaan dalam awal musim hujan atau musim kemarau. Selain ENSO, juga terdapat gejala anomali iklim yang dihasilkan oleh interaksi antara laut dan atmosfer di Samudra Hindia di sekitar khatulistiwa, yang disebut IOD (Indian Ocean Dipole). Selain melihat dampak anomali iklim terhadap lingkungan, studi ini juga menguji perbandingan hubungan antara anomali perubahan iklim dan curah hujan menggunakan metode SPSS dan bahasa pemrograman Python dengan membandingkan akurasi output yang dihasilkan secara komputasi. Pengaruh IOD dan ENSO terhadap wilayah tipe hujan ekatorial tidak cukup signifikan. Hubungan antara IOD dan ENSO tidak cukup kuat untuk wilayah khatulistiwa dan tidak terjadi pergeseran pada puncak onset di wilayah ini. Abstract. ENSO (El Niño -Southern Oscillation) is a form of climate deviation in the Pacific Ocean which is characterized by an increase in sea surface temperature (SST) in the Central and Eastern parts of the equator. This phenomenon plays an important role in annual and seasonal climate variations in Indonesia, especially in the equatorial region of Indonesia. This study aimed to analyze the impact of a computational climate anomaly in the equatorial region of Indonesia. The method used in this study was the linear regression method by comparing two statistical engines, namely the Python coding language and SPSS. The influence of ENSO is felt in several areas of Indonesia which are characterized by lower amounts of rainfall during the ENSO year compared to pre- and post-ENSO. El-Niño events also affect the entry of the dry season and its duration throughout the evolution of UNSO. Reducing the amount of annual rainfall is negatively correlated with increasing the number of forest fires per year. Machine learning-based rainfall analysis using google collab and python gives identical results to SPSS-based analysis, so the results of machine learning-based analysis have an accurate value. Climate change will result in changes in annual and interannual climate patterns such as a delay in the start of the rainy season or dry season. In addition, the rainy season period is also expected to be shorter. Apart from ENSO, there are also symptoms of climate deviations produced by the interaction of the sea and the atmosphere in the Indian Ocean around the equator, which is called the IOD (Indian Ocean Dipole). In addition to looking at the impact of climate anomaly on the environment, this study also examines the comparison of the relationship between climate change anomaly and rainfall using the SPSS method and the Python coding language by comparing the accuracy of the computationally generated output. The influence of IOD and ENSO on the rain-type region with the Equatorial rain-type region is not significant enough. The relationship between IOD and ENSO is not strong enough for the equatorial region and there is no shift in the peak onset in this region.

El Niño-Southern Oscillation (ENSO) is caused by periodic fluctuations in sea surface temperature and overlying air pressure across the Equatorial Pacific region. ENSO has a global impact on weather patterns and can cause severe weather events, such as heat waves, floods, and droughts, affecting regions far beyond the tropics. Therefore, forecasting ENSO with longer lead times is of great importance. This study utilizes Long Short-Term Memory (LSTM) network to predict ENSO events in the coming year based on environmental variables from previous years, including sea-surface temperature, sea level pressure, zonal wind, meridional wind, and zonal wind flux. These environmental variables are collected only inside certain spatial and temporal windows and used to forecast ENSO events. Furthermore, this study investigates how the size of these spatial and temporal windows influences the generalization accuracy of forecasting ENSO events. The size of spatial and temporal windows is optimized based on the generalization accuracy of the LSTM network in forecasting ENSO events. Our results indicated that the accuracy of the ENSO forecast is significantly sensitive to the extent of spatial and temporal windows. Specifically, increasing the temporal window size from one to nine years and the spatial window from 0 to 17.7 geographical degrees resulted in generalization accuracies, ranging from 40.1% to 83% in forecasting Central Pacific ENSO and 39.2% to 65% in forecasting Eastern Pacific ENSO. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO), a natural phenomenon in the Pacific Ocean, is caused by cyclic changes in sea-surface temperature (SST) and the overlying atmosphere in the tropical Pacific. The impact of ENSO varies, ranging from slightly warmer or colder temperatures to extreme weather events such as flash floods, droughts, and hurricanes, affecting various regions around the globe. Therefore, ENSO forecasting has paramount importance in the atmospheric and oceanic sciences. The Oceanic Niño Index (ONI), a three-month running mean of SST anomalies over the east–central equatorial Pacific region, is the commonly used metric for measuring ENSO events. However, the literature shows that the forecasting accuracy of ONI for lead times exceeding one year is low. This study aims to improve the forecast accuracy of ONI for up to 18 months lead time by applying an Adaptive Graph Convolutional Recurrent Neural Network (AGCRNN). The graph-learning module adaptively learns the spatial structure of features during training, while the graph convolution in hidden layers of the recurrent neural network captures the temporal relationships of features with ONI. Experiments conducted on simulation and reanalysis datasets demonstrate that AGCRNN outperforms state-of-art statistical and eight dynamical models for forecasting ONI with up to 18 months' lead time. [ABSTRACT FROM AUTHOR]

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Two persistent and heavy haze episodes during the COVID‐19 lockdown (from 20 Jan to 22 Feb 2020) still occur in northern China, when anthropogenic emissions, particularly from transportation sources, are greatly reduced. To investigate the underlying cause, this study comprehensively uses in‐situ measurements for ambient surface pollutants, reanalysis meteorological data and the WRF‐Chem model to calculate the contribution of NOx emission change and weather‐climate change to the "unexpectedly heavy" haze. Results show that a substantial NOx reduction has slightly decreased PM2.5 concentration. By contrast, the weakest East Asian winter monsoon (EAWM) in the 2019–2020 winter relative to the past decade is particularly important for haze occurrence. A warmer and moister climate is also favorable. Model results suggest that climate anomalies lead to a 25–50 μg m−3 increase of PM2.5 concentration, and atmospheric transport is also an important contributor to two haze episodes. The first haze is closely related to the atmospheric transport of pollutants from NEC to the south, and fireworks emissions in NEC are a possible amplifying factor that warrants future studies. The second one is caused by the convergence of a southerly wind and a mountain wind, resulting in an intra‐regional transport within BTH, with a maximal PM2.5 increment of 50–100 μg m−3. These results suggest that climate change and regional transport are of great importance to haze occurrence in China, even with significant emission reductions of pollutants. Plain Language Summary: During the COVID‐19 lockdown (from 20 Jan to 22 Feb 2020), anthropogenic emissions, particularly from transportation sources, are significantly reduced. However, there are still two heavy haze pollution events occurred in northern China. Our modeling study constrained by measurements show that the substantial NOx reduction only leads to a slight decrease in PM2.5 concentration. In contrast, the weakest East Asian winter monsoon in the 2019–2020 winter relative to the past decade is very important for the haze occurrence. Climate anomalies contribute 25–50 μg m−3 to the PM2.5 concentration. Atmospheric transport such as the transport of pollutants from Northeast China to the south and the intra‐regional transport within the Beijing‐Tianjin‐Hebei, also plays an important role in the haze formation. The results suggest that weather‐climate anomalies and regional transport can significantly worsen air quality during the COVID‐19 lockdown in northern China, in spite of significant reductions in air pollutants and precursors. Key Points: Emission reduction and two heavy haze episodes simultaneously occur in northern China during the COVID‐19 lockdownAnomalously warm and moist climate with a weak East Asian Winter Monsoon, and regional atmospheric transport contribute to COVID‐19 hazeA substantial reduction of NOx emission slightly mitigates haze during the lockdown period [ABSTRACT FROM AUTHOR]

In September 2007, Arctic sea ice plummeted to a shocking record minimum at the time. The amount of ice lost that summer was equal to that lost over the previous 25 years. As Arctic sea ice escapes scientists' predictions, scholars in the social sciences and humanities have critically interrogated "nature"/"culture" divides that treat the time of nature as unchanging and distinct from human beings. This essay examines what concept of time emerges through Arctic sea ice as an analytic lens. By this, I mean scientific knowledge of sea ice and the conceptual possibilities for thinking ice temporalities and environmental time-reckoning that it opens up. Attending to these possibilities suggests different kinds of "clocks" to help reckon the time of environmental changes in the form of (1) climate anomalies (e.g., deviations in ice thickness), which offer a different way of telling environmental time that attends to the physical specificity of substances; and (2) the Arctic Oscillation, a semi-periodic world weather pattern that emerges from the thick of relationships among ice, atmosphere, ocean, and now humans, generating a collective planetary time. Finally, I argue that the relational human–nonhuman production of planetary time shifts the focus in social studies of time from collective time-reckoning, which assumes entities have a socioculturally determined concept of time, toward temporal coordination as a less anthropocentric mode of ordering shared realities. Coordination decenters "the Human" as an epistemic ordering principle and enlarges ordering to include a diversity of nonhuman ways of being. Through temporal coordination, environmental prediction would be the ordering of a collective reality that a multiplicity of human and nonhuman ways of being make together rather than the search for a more precise clock, or development of better technoscientific means to capture nonhuman temporalities external to human beings. [ABSTRACT FROM AUTHOR]

It is well known that ancient Chinese written records are an invaluable source of historical information on astronomy, technology, economy, and climate, dating back in some cases to the second millennium BCE. Numerous studies have established that the climatic consequences of the ejection of volcanic aerosols into the atmosphere can have a major impact on the climate worldwide. Here, the Chinese evidence of such a severe climatic anomaly during the decade of the 530s is first reviewed. A preponderance of the evidence from ice cores and tree rings worldwide points to more than one large volcanic eruption during the 530s, although which volcano was responsible has not yet been conclusively established. Even more severe than 1815, the "year without a summer," due to the eruption of Tambora, in 536–537 summer frosts and snowfall occurred in China causing multi-year drought, crop failures, and catastrophic famine. Study of pre-imperial Chinese texts gives reason to believe that in mid-second millennium BCE a massive loading of the atmosphere with volcanic aerosols from the eruption of Thera (Santorini) may have been the cause of dramatic climatic downturn in the sixteenth century BCE. Dating of the events based on verifiable records of astronomical phenomena suggests that the long-remembered ancient calamity could have been caused by the eruption of Thera in the eastern Mediterranean. This benchmark date can be helpful in establishing a secure chronology of eastern Mediterranean kingdoms in the period. [ABSTRACT FROM AUTHOR]

Understanding precipitation and its relation with atmospheric and oceanic conditions is vital in the face of climate change. This is crucial in the Tropical Andes (TA) because millions of people depend on water originated in the cordillera. Unfortunately, the paucity of meteorological monitoring that exists in mountainous regions is accentuated in the tropics. In this context, climate indices, remotely sensed, and gridded datasets, are useful tools to study climate and precipitation in the TA, and additional climate indices can be calculated from reanalysis datasets. The combination of this information with traditional indices has the potential to improve our understanding of precipitation. Our objective was to use the k‐means algorithm to regionalize precipitation in the TA (different regions have different climate), and then use the random forest algorithm to study the variables related to precipitation in each of these regions in seasonal timescales. Here, we show the suitability of the random forest algorithm to reveal climate processes and the high potential of the novel climate indices to improve the regressions. We found that convective available potential energy was the most important variable for precipitation in the northern TA, except for the Chocó, where v at 850 hPa was the most important one. Meanwhile, vertical integral of divergence of moisture flux was the most important one in the southern TA. Interestingly, in the DJF season when the South American low‐level jet (SALLJ) is more active, u and v at 850 hPa showed their lowest relative importance and the total column of water vapour showed its maximum, this could indicate that precipitation anomalies are controlled by atmospheric moisture availability rather than by the speed of the SALLJ during DJF. These results deepen our understanding of precipitation anomalies in the TA and the related oceanic and atmospheric conditions. The proposed methodology was proven to be suitable and it could be used in the future to test and formulate new hypotheses, and to forecast seasonal precipitation. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) is caused by periodic fluctuations in sea surface temperature and overlying air pressure across the Equatorial Pacific region. ENSO has a global impact on weather patterns and can cause severe weather events, such as heat waves, floods, and droughts, affecting regions far beyond the tropics. Therefore, forecasting ENSO with longer lead times is of great importance. This study utilizes Long Short-Term Memory (LSTM) network to predict ENSO events in the coming year based on environmental variables from previous years, including sea-surface temperature, sea level pressure, zonal wind, meridional wind, and zonal wind flux. These environmental variables are collected only inside certain spatial and temporal windows and used to forecast ENSO events. Furthermore, this study investigates how the size of these spatial and temporal windows influences the generalization accuracy of forecasting ENSO events. The size of spatial and temporal windows is optimized based on the generalization accuracy of the LSTM network in forecasting ENSO events. Our results indicated that the accuracy of the ENSO forecast is significantly sensitive to the extent of spatial and temporal windows. Specifically, increasing the temporal window size from one to nine years and the spatial window from 0 to 17.7 geographical degrees resulted in generalization accuracies, ranging from 40.1% to 83% in forecasting Central Pacific ENSO and 39.2% to 65% in forecasting Eastern Pacific ENSO.

El Niño-Southern Oscillation (ENSO), a natural phenomenon in the Pacific Ocean, is caused by cyclic changes in sea-surface temperature (SST) and the overlying atmosphere in the tropical Pacific. The impact of ENSO varies, ranging from slightly warmer or colder temperatures to extreme weather events such as flash floods, droughts, and hurricanes, affecting various regions around the globe. Therefore, ENSO forecasting has paramount importance in the atmospheric and oceanic sciences. The Oceanic Niño Index (ONI), a three-month running mean of SST anomalies over the east–central equatorial Pacific region, is the commonly used metric for measuring ENSO events. However, the literature shows that the forecasting accuracy of ONI for lead times exceeding one year is low. This study aims to improve the forecast accuracy of ONI for up to 18 months lead time by applying an Adaptive Graph Convolutional Recurrent Neural Network (AGCRNN). The graph-learning module adaptively learns the spatial structure of features during training, while the graph convolution in hidden layers of the recurrent neural network captures the temporal relationships of features with ONI. Experiments conducted on simulation and reanalysis datasets demonstrate that AGCRNN outperforms state-of-art statistical and eight dynamical models for forecasting ONI with up to 18 months’ lead time.

Litterfall is a major pathway for transferring aboveground biomass to the forest floor and thus plays an important role in building forest soil carbon stocks. However, inter- and intra-annual variability of litterfall remains poorly documented, especially in North American temperate and boreal forests, due to the lack of recent long-term studies at high sampling frequencies. This potentially creates uncertainties in estimates of forest carbon budget models. The objectives of the present study were to 1) quantify the mean annual flux, interannual variability, and seasonality of litterfall in three sites (dominated respectively by sugar maple (Acer saccharum Marsh.), balsam fir (Abies balsamea (L.) Mill. 1768), and black spruce (Picea mariana (Mill.) B.S.P.)) in eastern Canada over a period of 22–32 years, 2) relate the litterfall amounts and temporal variations to the changes in the size of major organic matter pools in these ecosystems, and 3) compare our litterfall estimates with reference values used in national greenhouse gas inventories. Litterfall production decreased from the sugar maple to the balsam fir and black spruce sites, preponderantly due to species composition. Litterfall evolution was related to the aboveground biomass of live trees in both conifer sites; in contrast, in the broadleaf site, changes in forest composition and structure were apparently the main drivers. The litterfall seasonality varied between broadleaf and conifer sites and could be explained by a sigmoidal model. Substantial departures from the seasonality for some given years were likely due to important climatic anomalies. Forest floor biomass remained stable over time at all three sites despite the increase in litterfall at the balsam fir and sugar maple sites and rapid forest floor turnover at the latter site. Our analyses of litterfall suggest that reference values from the literature used for national greenhouse gas inventories underestimate annual litterfall and forest floor carbon stocks for temperate and boreal forests. [Display omitted] • Litterfall evolution was related to aboveground biomass only in conifer sites • Mean annual flux and seasonality of litterfall varied by forest type • Climatic anomalies altered annual litterfall production and seasonality • Carbon budget model underestimated carbon stock of litterfall and forest floor [ABSTRACT FROM AUTHOR]

• We analyzed two decades of anomalies of NDVI, precipitation and temperature. • Temperature and precipitation exert positive effects on vegetation greenness. • Lower landscape greenness seems related to droughts and extreme frost events. • Landscape greenness increased steadily after the 2011 extreme frost disturbance. Canopy greenness is an indicator of ecosystem primary productivity, which is often limited by temperature and precipitation. Changes in vegetation greening have been reported mostly at global scales. However, we still have a poor understanding of vegetation greening patterns and drivers for major vegetation types, such as the tropical dry forest, one of the most extensive vegetation types in Mexico. Here, we analyze two decades of interannual variation in greenness and its relationship to temperature and precipitation in the northmost neotropical dry forest occurring in Northwestern Mexico. We constructed time-series linear regression models using standardized anomalies with z-scores (i.e., standard deviations away from the long-term mean) for the Normalized Difference Vegetation Index (NDVI) and the climate data from 2001 to 2021. Our best models indicate both temperature and precipitation exert positive effects on vegetation greenness, particularly from a lagged effect perspective, as retained predictors were the accumulated precipitation of two monsoon (summer) seasons and previous year mean temperature. The lowest levels of landscape greenness seem connected to prolonged droughts and extreme frost events. In fact, a switch from negative to positive NDVI anomalies was observed in the years following the February 2011 extreme frost that affected much of North America, including northern Mexico. Notable, under the stricter statistical criterion of −1.7 ≥ z score ≥ 1.7, only climatic variables presented very-extreme anomalies and these were not necessarily linked to a very-extreme response in landscape greenness. However, considering the criterion of −1.3 ≥ z score ≥ 1.3, we identified several extreme NDVI anomalies and their corresponding climatic anomaly. Therefore, more flexible statistical criteria might reveal climate extremes of ecological and social relevance. Our overall findings have implications for forest and climate risk management, particularly as extreme climatic anomalies are expected to continue increasing in light of climate change. [ABSTRACT FROM AUTHOR]

The El Niño-Southern Oscillation (ENSO) phenomenon affects the global climate by changing temperature and precipitation patterns mainly in tropical climatic regions and median latitudes. Such event strongly influences agricultural activities and crop yields. The Niño Oceanic Index (ONI) of the US National Oceanic and Atmospheric Administration (NOAA) describes and monitors ENSO intensity from ocean temperature measurements. When ONI in the Niño 3.4 region was + 0.5 °C above normal or − 0.5 °C below normal for 5 consecutive 3-month running averages, El Niño (EN) or La Niña (LN) events, respectively, were established. The prediction of ENSO events is made by modeling at major global weather centers by atmosphere–ocean coupling models; however, no articles were found using decision tree classifier (DTC) for ENSO forecasting purposes. This modeling approach requires much less computational time and capacity. Furthermore, DTC can be sufficiently accurate for agricultural purposes. Thus, the objective of this research was to forecast as early as possible the El Niño and La Niña yearly events using a DTC technique from ONI data from 1950 to 2020. We used as input variables for DTC quarterly ONI values from 15 quarters prior the data of forecasting. The DTC showed an accuracy of 89%, 84%, and 78% to predict La Niña, El Niño, and neutral years, respectively, without training period. For validation, the accuracy was 100%, 79%, and 79% for La Niña, El Niño, and neutral years, respectively. The selected ONI quarters were July–August-September, January–February-March, and February–March-April of the previous year and January–February-March of the current year, allowing an 8-month advance forecast with an average accuracy of 78% (validation). [ABSTRACT FROM AUTHOR]

A better understanding of how climate affects growth in tree species is essential for improved predictions of forest dynamics under climate change. Long‐term climate averages (mean climate) drive spatial variations in species' baseline growth rates, whereas deviations from these averages over time (anomalies) can create growth variation around the local baseline. However, the rarity of long‐term tree census data spanning climatic gradients has so far limited our understanding of their respective role, especially in tropical systems. Furthermore, tree growth sensitivity to climate is likely to vary widely among species, and the ecological strategies underlying these differences remain poorly understood. Here, we utilize an exceptional dataset of 49 years of growth data for 509 tree species across 23 tropical rainforest plots along a climatic gradient to examine how multiannual tree growth responds to both climate means and anomalies, and how species' functional traits mediate these growth responses to climate. We show that anomalous increases in atmospheric evaporative demand and solar radiation consistently reduced tree growth. Drier forests and fast‐growing species were more sensitive to water stress anomalies. In addition, species traits related to water use and photosynthesis partly explained differences in growth sensitivity to both climate means and anomalies. Our study demonstrates that both climate means and anomalies shape tree growth in tropical forests and that species traits can provide insights into understanding these demographic responses to climate change, offering a promising way forward to forecast tropical forest dynamics under different climate trajectories. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) is a climate phenomenon caused due to irregular periodic oscillation in easterly winds and sea surface temperature (SST) over the tropical Pacific Ocean. ENSO is one of the main drivers of Earth's inter-annual climate variability, which causes climate anomalies in the form of tropical cyclones, severe storms, heavy rainfalls and droughts. Due to the impact of ENSO on global climate, forecasting ENSO is of great importance. However, forecast accuracy of ENSO for a lead time of one year is low. ENSO events are forecasted through Oceanic Niño Index (ONI), which is the three-month running mean of SST anomalies over the Niño 3.4 region (5∘N-5∘S, 120∘W-170∘W). Features, such as SST, sea level pressure, zonal wind speed and meridional wind speed that contribute in determining ONI are mapped on spatial or geographical grids, where each spatial or geographical grid represents the values of one feature at a snapshot. Juxtaposing the spatial grids of all features creates a layered map at a snapshot. The layered spatial feature map is constructed at different snapshots, and they all are fed to the CLSTM to forecast ONI at lead times of 1, 3, 6, 9 and 12 months. This study employs backward stepwise feature selection based on generalization accuracy to find the most effective features. SST showed to be the best feature for forecasting ONI. Experiments showed that the CLSTM outperforms Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Standard Neural Network (SNN) in terms of coefficient of determination ( R 2 ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). More specifically, an improvement in R 2 values by 27.6%, 20.9%, 25% and 15.2% over CNN is observed for lead times of 3, 6, 9 and 12 months, respectively. [ABSTRACT FROM AUTHOR]

The paper analyzes the climatic anomalies in Oltenia that occurred in spring 2020. After the Mediterranean winter of 2019-2020, the spring was excessively early on large areas, and on average very early. As a result, the vegetation started to develop very early, since the first part of March, and the flowering of the early fruit trees took place in the first half of March. In the first two months of spring there were 31 days in which the daily minimum temperatures were negative and there was hoar and frost on the soil surface. Thus, in March, minimum negative temperatures were registered in the intervals: 1.III, 6.III, 16-19.III, 23-31.III, ie 15 days. In April, minimum negative temperatures were registered in the intervals: 1-10.IV, 15-16. IV, 23-25.IV and 27.IV, totaling 16 days. The cooling of the weather culminated with the interval 22-25.III, in which the highest amounts of precipitation were registered in March but also in the whole cold season 2019-2020. There were blizzards that deposited a consistent layer of snow and banks formed, lasting 4 days being the longest in the cold season 2019-2020. The intense cooling of the weather after the warm winters are destructive climatic anomalies. The paper is part of an extensive series of studies on climate variability in southwestern Romania (Oltenia) in the context of climate change (I. Marinică, 2006, 2008; Marinicǎ I., Marinicǎ Andreea Floriana, 2016).

Geospatial technology has an enormous capacity to analyze large and diversified datasets for evaluating the hidden spatial relationship which provides a better comprehension of the subject and helps significantly in policymaking and planning future strategies.

We explore the effect of individual perceptions of climate anomalies on collective action within a context of environmental complexity and uncertainty. To do so, we construct two competing propositions that are theoretically robust but with very different real-world implications. Our first proposition suggests that collective action to adapt to climate change is likely to be more effective when perceptions of climate anomalies converge within a community. Our second proposition suggests the opposite: that convergence is likely to hinder adaptation behaviour. We use a community co-designed measure of perceptions and an artefactual field experiment to test our propositions and explore the effect of perception convergence on climate change adaptation behaviour in six communities in Malaysian Borneo. We find a robust positive relationship between convergent perceptions of climate anomalies and the collective action required to adapt to climate change. Our findings suggest that perception convergence is an underexplored and potentially crucial factor that can either drive or hinder adaptation efforts at the community-level.

In this paper, a new decadal resolution stalagmite δ18O record covering 10.4–6.5 ka BP from Kulishu cave in Beijing, north China is presented in combination with the published stalagmite δ18O record covering 10.4–14.0 ka BP in the same cave. Five significant monsoon collapses were identified around 11.5, 11.0, 10.0, 9.4, and 8.2 ka BP as well as three smaller ones around 10.3, 9.0, and 8.6 ka BP. The weak monsoon episodes around 8.6 and 8.2 ka BP form the two-step structure of the 8.2 ka event. All monsoon collapses, coeval with the cooling in northern high-latitude records, are correlated with Lakes Agassiz-Ojibway outbursts. Thus, our data support the idea of freshwater forcing of abrupt climate anomalies during the early Holocene. Nevertheless, the decreased irradiance together with freshwater outburst may account for the 9.2/9.3 ka event, which is expressed more significantly in low-latitude records. [ABSTRACT FROM AUTHOR]

The seasonal prediction skills in the CAMS-CSM (the acronym stands for the Chinese Academy of Meteorological Sciences Climate System Model) climate forecast system is evaluated with a set of retrospective forecast experiments during the period of 1981–2019. The CAMS-CSM, which has been registered for the sixth phase of the coupled model intercomparison project (CMIP6), is an atmosphere–ocean–land–sea ice fully coupled general circulation model. The assimilation scheme used in the forecast system is the 3-dimentional nudging, including both the atmospheric and oceanic components. The analyses mainly focus on the seasonal predictable skill of sea surface temperature, 2-m air temperature, and precipitation anomalies. The analyses revealed that the model shows a good prediction skill for the SST anomalies, especially in the tropical Pacific, in association with El Niño-Southern Oscillation (ENSO) events. The anomaly correlation coefficient (ACC) score for ENSO can reach 0.75 at 6-month lead time. Furthermore, the extreme warm/cold Indian Ocean dipole (IOD) events are successfully predicted at 3- and even 6-month lead times. The whole ACC of IOD events between the observation and the prediction can reach 0.51 at 2-month lead time. There are reliable seasonal prediction skills for 2-m air temperature anomalies over most of the Northern Hemisphere, where the correlation is mainly above 0.4 at 2-month lead time, especially over the East Asia, North America and South America. However, the seasonal prediction for precipitation still faces a big challenge. The source of precipitation predictability over the East Asia can be partly related to strong ENSO events. Additionally, the anomalous anticyclone over the western North Pacific (WPAC) which connects the ENSO events and the East Asian summer monsoon (EASM) can be well predicted at 6-month lead time. [ABSTRACT FROM AUTHOR]

The dynamic changes in the regimes of forest fires are due to the severity of climate and weather factors. The aim of the study was to examine the trend of forest fires and to evaluate their relationship with climate parameters for the state of Telangana in India. The climate and forest fire data were used and uploaded to the GIS platform in a specified vector grid (spacing: 0.3° x 0.3°). The data were evaluated spatially and statistical methods were applied to examine any relationships. The study revealed that there was a 78% incidence of forest fires in the months of February and March. The overall forest fire hotspot analysis (January to June) of grids revealed that the seven highest forest fire grids retain fire events greater than 600 were found in the north east of Warangal, east of Khammam and south east of Mahbubnagar districts. The forest fire analysis significantly followed the month wise pattern in grid format. Ten grids (in count) showed a fire frequency greater than 240 in the month of March and of these, three grids (in count) were found to be common where the forest fire frequency was highest in the preceding month. Rapid seasonal climate/weather changes were observed which significantly enhanced the forest fire events in the month of February onwards. The solar radiation increased to 159% in the month of March when compared with the preceding month whereas the relative humidity decreased to 47% in the same month. Furthermore, the wind velocity was found to be highest (3.5 meter/sec.) in the month of February and precipitation was found to be lowest (2.9 mm) in the same month. The analysis of Cramer V coefficient (CVC) values for wind velocity, maximum temperature, solar radiation, relative humidity and precipitation with respect to fire incidence were found to be in increasing order and were in the range of 0.280 to 0.715. The CVC value for precipitation was found to be highest and equivalent to 0.715 and showed its strongest association/relationship with fire events. The significant increase in precipitation not only enhances the moisture in the soil but also in the dry fuel load lying on the forest floor which greatly reduces the fuel burning capacity of the forest. The predicted (2050) temperature anomalies data (RCP-6) for the month of February and March also showed a significant increase in temperature over those areas where forest fire events are found to be notably high in the present scenario which will certainly impact adversely on the future forest fire regime. Findings from this study have their own significance because such analyses/relationships have never be examined at the state level, therefore, it can help to fulfill the knowledge gap for the scientific community and the state forest department, and support fire prevention and control activities. There is a need to replicate this study in future by taking more climate variables which will certainly give a better understanding of forest fire events and their relationships with various parameters. The satellite remote sensing data and GIS have a strong potential to analyze various thematic datasets and in the visualization of spatial/temporal paradigms and thus significantly support the policy making framework.

Abstract The “United Nations 2030 Agenda for Sustainable Development” adopted in 2015 covers 17 Sustainable Development Goals (SDGs). Fire is a common form of disaster and has a wide range of impacts on climate, biogeochemical cycle and human health, highly related to the SDGs. Satellite remote sensing technology provides an effective way for the dynamic monitoring of global burned area (BA). However, the existing global BA products are mainly at low and medium spatial resolution, which is difficult for detecting small fires, and also has large errors in calculating area of burned land. Based on Landsat 8 surface reflectance data and Google Earth Engine platform, a novel multi‐year (from the starting year of SDGs [2015] to the nearest year [2019]) 30 m resolution global BA products were generated. Based on these products, spatial distribution, influencing factors and change characteristics of BA were analyzed. The results show that from 2015 to 2019 the total area of BA in the world was 365.27, 368.56, 374.56, 345.55, and 363.09 × 106 ha. From 2015 to 2019, the total area of global BA was relatively stable, however there were significant differences among various continents and regions. During the study period, BA in the Amazon and Australia changed dramatically. The huge Amazon fire in 2019 caused a reduction of 4.67 × 106 ha of vegetated area in the Amazon basin, an increase in BA of 119.25% compared with 2018. In 2019, BA on the eastern and southeastern coasts of Australia increased abnormally. The possible reasons for these changes were comprehensively discussed and analyzed.

The "United Nations 2030 Agenda for Sustainable Development" adopted in 2015 covers 17 Sustainable Development Goals (SDGs). Fire is a common form of disaster and has a wide range of impacts on climate, biogeochemical cycle and human health, highly related to the SDGs. Satellite remote sensing technology provides an effective way for the dynamic monitoring of global burned area (BA). However, the existing global BA products are mainly at low and medium spatial resolution, which is difficult for detecting small fires, and also has large errors in calculating area of burned land. Based on Landsat 8 surface reflectance data and Google Earth Engine platform, a novel multi‐year (from the starting year of SDGs [2015] to the nearest year [2019]) 30 m resolution global BA products were generated. Based on these products, spatial distribution, influencing factors and change characteristics of BA were analyzed. The results show that from 2015 to 2019 the total area of BA in the world was 365.27, 368.56, 374.56, 345.55, and 363.09 × 106 ha. From 2015 to 2019, the total area of global BA was relatively stable, however there were significant differences among various continents and regions. During the study period, BA in the Amazon and Australia changed dramatically. The huge Amazon fire in 2019 caused a reduction of 4.67 × 106 ha of vegetated area in the Amazon basin, an increase in BA of 119.25% compared with 2018. In 2019, BA on the eastern and southeastern coasts of Australia increased abnormally. The possible reasons for these changes were comprehensively discussed and analyzed. Plain Language Summary: Fire is a common form of disaster, and has a wide range of impacts on climate, surface vegetation and human health. The "United Nations 2030 Agenda for Sustainable Development" has been implemented for 5 years, and fire is highly related to the Sustainable Development Goals, for which global information on fire effects is a key variable. In order to carry out high‐precision and fine monitoring of the spatial distribution and changes of fire within the past 5 years, in this study 30 m global burned area (BA) products were generated and analyzed from 2015 to 2019. During the study period, we found that the total area of global BA was relatively stable, however there were significant differences among various continents and regions. For example, the huge Amazon fire in 2019 caused a reduction of 4.67 × 106 ha of vegetated area in the Amazon basin, an increase in BA of 119.25% compared with 2018. In 2019, BA on the eastern and southeastern coasts of Australia increased abnormally. The possible reasons for these changes were comprehensively discussed and analyzed. Key Points: Novel multi‐year (from 2015 to 2019) 30 m resolution global burned area products were generated and acted as an Sustainable Development Goals indicatorSpatial distribution, influencing factors and change characteristics of burned areas were discussed from different perspectivesBurned areas in the Amazon and Australia changed dramatically. The possible reasons for these changes were comprehensively discussed [ABSTRACT FROM AUTHOR]

The paper analyzes the climatic anomalies in Oltenia that occurred in spring 2020. After the Mediterranean winter of 2019-2020, the spring was excessively early on large areas, and on average very early. As a result, the vegetation started to develop very early, since the first part of March, and the flowering of the early fruit trees took place in the first half of March. In the first two months of spring there were 31 days in which the daily minimum temperatures were negative and there was hoar and frost on the soil surface. Thus, in March, minimum negative temperatures were registered in the intervals: 1.III, 6.III, 16-19.III, 23-31.III, ie 15 days. In April, minimum negative temperatures were registered in the intervals: 1-10.IV, 15-16. IV, 23-25.IV and 27.IV, totaling 16 days. The cooling of the weather culminated with the interval 22-25.III, in which the highest amounts of precipitation were registered in March but also in the whole cold season 2019-2020. There were blizzards that deposited a consistent layer of snow and banks formed, lasting 4 days being the longest in the cold season 2019-2020. The intense cooling of the weather after the warm winters are destructive climatic anomalies. The paper is part of an extensive series of studies on climate variability in southwestern Romania (Oltenia) in the context of climate change (I. Marinică, 2006, 2008; Marinica I., Marinica Andreea Floriana, 2016). [ABSTRACT FROM AUTHOR]

The impact of solar activity on climate system is spatiotemporally selective and usually more significant on the regional scale. Using statistical methods and solar radio flux (SRF) data, this paper investigates the impact of the solar 11-yr cycle on regional climate of Northeast Asia in recent decades. Significant differences in winter temperature, precipitation, and the atmospheric circulation over Northeast Asia are found between peak and valley solar activity years. In peak years, temperature is higher over vast areas of the Eurasian continent in middle and high latitudes, and prone to producing anomalous high pressure there. Northeast Asia is located to the south of the anomalous high pressure, where the easterlies prevail and transport moisture from the western Pacific Ocean to the inland of East Asia and intensify precipitation there. In valley years, temperature is lower over the Eurasian continent and northern Pacific Ocean in middle and high latitudes, and there maintain anomalous low pressure systems in the two regions. Over the Northeast Asian continent, north winds prevail, which transport cold and dry air mass from the high latitude to Northeast Asia and reduce precipitation there. The correlation coefficient of winter precipitation in Northeast China and SRF reaches 0.4, and is statistically significant at the 99% confidence level based on the Student's t-test. The latent heat flux anomalies over the Pacific Ocean caused by solar cycle could explain the spatial pattern of abnormal winter precipitation of China, suggesting that the solar activity may change the climate of Northeast Asia through air-sea interaction. [ABSTRACT FROM AUTHOR]

Analyzing the forest fires events in climate change scenario is essential for protecting the forest from further degradation. Geospatial technology is one of advanced tools that has enormous capacity to evaluate number of data sets simultaneously and analyze the hidden relationships and trends. This study has evaluated the long term forest fire events with respect to India's state boundary, its seasonal monthly trend, all forest categories of LULC and future climate anomalies datasets over the Indian region. Furthermore, the spatial analysis revealed the trend and their relationship. The state wise evaluation of forest fire events reflects that the state of Mizoram has the highest forest fire frequency percent (11.33%) followed by Chhattisgarh (9.39%), Orissa (9.18%), Madhya Pradesh (8.56%), Assam (8.45%), Maharashtra (7.35%), Manipur (6.94%), Andhra Pradesh (5.49%), Meghalaya (4.86%) and Telangana (4.23%) when compared to total country forest fire counts. The various LULC categories which represent the forest show some notable forest fire trend. The category "Deciduous Broadleaf Forest" retain highest fire frequency equivalent to 38.1% followed by "Mixed Forest" (25.6%), "Evergreen Broadleaf Forest" (16.5%), "Deciduous Needle leaf Forest" (11.5%), "Shrub land" (5.5%), "Evergreen Needle leaf Forest" (1.5%) and "Plantations" (1.2%). Monthly seasonal variation of forest fire events revel the highest forest fire frequency percent in the month of "March" (55.4%) followed by "April" (28.2%), "February" (8.1%), "May" (6.7%), "June" (0.9%), "January" (0.7%). The evaluation of future climate data for the year 2030 shows significant increase in forest fire seasonal temperature and abrupt annual rainfall pattern therefore future forest fires will be more intensified in large part of India whereas it will be more crucial for some of the states such as Orissa, Chhattisgarh, Mizoram, Assam and in lower Sivalik range of Himalaya. The deciduous forests will further degrade in future. The highlight/results of this study have very high importance because such spatial relationship among the various datasets is analyzed at the country level in view of the future climate scenario. Such analysis gives insight to the policymaker to make sustainable future plans for prioritization of the various state forests suffering from forest fire keeping in mind the future climate change scenario. [ABSTRACT FROM AUTHOR]

The local hydrological and climatic impacts of Eurasian snowmelt are studied using advanced land surface and atmospheric data. It is found that intense melting of snow is located at mid-high latitudes in April and May. Snowmelt plays an important role in determining the seasonal cycles of surface runoff and soil moisture (SM). Specifically, melting is accompanied by sharp responses in surface runoff and surface SM while the impacts are delayed for deeper-layer of soil. This is particularly significant in the western sector of Eurasia. On interannual timescales, the responses of various surface parameters to snowmelt in the same month are rather significant. However, the persistence of surface SM anomalies is weak due to the strong soil evaporation anomalies and surplus of surface energy for evaporation. Strong impacts on the sensible heat flux, planetary boundary layer height and precipitation in the next month following the melting of snow are identified in west Russia and Siberia. Downward propagation of surface SM anomalies is observed and a positive evaporation-convection feedback is identified in west Russia. However, the subsequent impacts on the local convective precipitation in late spring-summer and its contribution to the total precipitation are seemingly weak. The atmospheric water vapor convergence has strong control over the total precipitation anomalies. Overall, snowmelt-produced SM anomalies are not found to significantly impact the late spring-summer local climate anomalies in Northern Eurasia. Therefore, the delayed remote-responses of atmospheric circulation and climate to the melting of Eurasian snow may be only possible near the melting period. [ABSTRACT FROM AUTHOR]

High‐resolution, spatially continuous climate models are an essential first step in assessing fine‐scale impacts of climate and projected future climates. Observational weather station networks supporting such models are often inadequate and are spread unevenly throughout the globe, particularly in developing regions such as Madagascar, since network establishment and maintenance requires significant investment. Quality climate models are greatly needed for the assessment of conservation, agricultural productivity and socio‐economic studies of climate vulnerability. With the aim of generating improved modern climate normal surfaces for Madagascar, we have developed a method for augmenting the existing sparse climate normal network with spatial patterns of climate change derived from more densely distributed historical climate normals. Thin plate smoothing splines and kriging were applied to the spatial analysis of the climate difference patterns and their performances were compared. We found that the augmented data improved the spatial consistency of the climate interpolations when compared with the interpolations using only the existing observed data. Our analysis confirmed the robustness and accuracy of thin plate spline interpolation when applied to long‐term differences in climate normals. Further, the investigation indicated that long‐term differences in rainfall normals have broader spatial coherence and are less susceptible to local differences when compared to temperature normals. This suggests that long term change patterns in rainfall are better supported by sparse data networks, despite the usually acknowledged greater inherent spatial and temporal variability of rainfall. The augmented data capture distinct spatial patterns in temperature and rainfall that differ markedly from the patterns in interpolations of observed data alone. The derived change patterns are consistent with observed global trends. This technique might be used to improve understanding of the current climate in other data‐poor regions and the outputs used in spatial environmental and socio‐economic studies, such as species distribution mapping and climate change resilience mapping. We present a new method to improve climate normal surfaces by augmenting current sparse data networks with spatial patterns of climate change derived from long‐term weather stations. The technique enhanced the spatial consistency of climate interpolations of Madagascar and captured distinct spatial patterns in temperature and rainfall that differ from patterns derived from observed data alone. This method may improve the current assessment of the climate in data‐poor regions to better inform environmental and socio‐economic studies. [ABSTRACT FROM AUTHOR]

Interannual variability of the North Pacific storm track and the three-dimensional atmosphere circulation during winter are investigated using NCEP/NCAR reanalysis data during 1950-2015. Results show that year-to-year variations of the storm track exhibit two principal modes, i.e. the monopole intensity change and the meridional shift of the storm track, respectively. The intensity change mode is linked to weakening of the Siberian high, northward shift of the western Pacific jet stream and Aleutian Low, and well corresponding to the Western Pacific teleconnection. The meridional shift mode is related to intensification and south-eastward extension of western Pacific jet stream and Aleutian Low, and linked to the Pacific-North America teleconnection. The internal atmospheric dynamics responsible for the storm track variability is further investigated from the perspective of wave-flow energy conversion. For the intensity change mode, accompanied by the enhanced baroclinity over the entrance region of the storm track, more energy is converted from mean available potential energy to eddy available potential energy and then transferred to eddy kinetic energy, which is favorable for the overall enhancement of the storm track intensity. For the meridional shift mode, more energy is transformed from mean available potential energy to eddy available potential energy and further transferred to eddy kinetic energy over the southern (northern) areas of the storm track, contributing to the southward (northward) shift of the storm track. Additionally, the increased (decreased) conversion from mean-flow kinetic energy to eddy kinetic energy over the north-eastern Pacific region is also in favor of the southward (northward) shift of the storm track. [ABSTRACT FROM AUTHOR]

Increases in temperature have been predicted and reported for the Mediterranean mountain ranges due to global warming and this phenomenon is expected to have profound consequences on biodiversity and ecosystem functioning. We hereby present the case of Gentiana lutea L. subsp. lutea, a rhizomatous long-lived plant living in Central-Southern Europe, which is at the edge of its ecological and distributional range in Sardinia. Concretely, we analysed the reproductive success experienced during three phenological cycles (2013/2014, 2014/2015 and 2015/2016) in four representative populations, with particular attention to the phenological cycle of 2014/2015, which has been recorded as one of the warmest periods of the last decades. The Smirnov-Grubbs test was used to evaluate differences in temperature and precipitation regimes among historical data and the analysed years, while the Kruskal-Wallis followed by the Wilcoxon test was used to measure differences between anthesis and reproductive performances among cycles and populations. In addition, generalised linear models were carried out to check relationships between climate variables and reproductive performance. Significant differences among climate variables and analysed cycles were highlighted, especially for maximum and mean temperatures. Such variations determined a non-flowering stage in two of the four analysed populations in 2014/2015 and significant differences of further five reproductive traits among cycles. These results confirmed that in current unstable climatic conditions, which are particularly evident in seasonal climates, reproductive success can be a sensitive and easily observable indicator of climatic anomalies. Considering the importance of this issue and the ease and cost-effectiveness of reproductive success monitoring, we argue that research in this sense can be a supporting tool for the enhancement of future crucial targets such as biodiversity conservation and the mitigation of global warming effects. [ABSTRACT FROM AUTHOR]

Abstract: Severe haze pollution in eastern China has caused substantial health impacts and economic loss. Conducive atmospheric conditions are important to affect occurrence of severe haze events, and circulation changes induced by future global climate warming are projected to increase the frequency of such events. However, a potential contribution of an anthropogenic influence to recent most severe haze (December 2015 and January 2013) over eastern China remains unclear. Here we show that the anthropogenic influence, which is estimated by using large ensemble runs with a climate model forced with and without anthropogenic forcings, has already increased the probability of the atmospheric patterns conducive to severe haze by at least 45% in January 2013 and 27% in December 2015, respectively. We further confirm that simulated atmospheric circulation pattern changes induced by anthropogenic influence are driven mainly by increased greenhouse gas emissions. Our results suggest that more strict reductions in pollutant emissions are needed under future anthropogenic warming. [ABSTRACT FROM AUTHOR]

Strong El Niño events alter tropical climates and may lead to a negative carbon balance in tropical forests and consequently a disruption to the global carbon cycle. The complexity of tropical forests and the lack of data from these regions hamper the assessment of the spatial distribution of El Niño impacts on these ecosystems. Typically, maps of climate anomaly are used to detect areas of greater risk, ignoring baseline climate conditions and forest cover. Here, we integrated climate anomalies from the 1982−1983, 1997−1998, and 2015−2016 El Niño events with baseline climate and forest edge extent, using a risk assessment approach to hypothetically assess the spatial and temporal distributions of El Niño risk over tropical forests under several risk scenarios. The drivers of risk varied temporally and spatially. Overall, the relative risk of El Niño has been increasing driven mainly by intensified forest fragmentation that has led to a greater chance of fire ignition and increased mean annual air temperatures. We identified areas of repeated high risk, where conservation efforts and fire control measures should be focused to avoid future forest degradation and negative impacts on the carbon cycle.