Annual Anomalies Research Articles

Interannual variability in net ecosystem carbon production in a rain-fed maize ecosystem and its climatic and biotic controls during 2005-2018.

Interannual variability (IAV) in net ecosystem carbon production (NEP) plays an important role in the processes of the carbon cycle, but the long-term trends in NEP and the climatic and biotic control of IAV in NEP still remain unclear in agroecosystems. We investigated interannual variability in NEP, expressed as annual values and anomalies, and its climatic and biotic controls using an eddy-covariance dataset for 2005–2018 for rain-fed spring maize in northeastern China. Average annual NEP was 270±31 g C m−2yr −1, with no significant changes over time. The effects on interannual variability in NEP of gross ecosystem productivity (GEP) that was mainly controlled by soil water content (SWC) and leaf area index (LAI), were more than those of respiration (RE) that was controlled by temperature and LAI. Further, maximum daily NEP (NEPmax) that was dominated by summer vapor pressure deficit explained the largest fraction of annual anomalies in NEP, followed by carbon dioxide uptake period (CUP) that was defined by the beginning date (BDOY) and the end date (EDOY) of CUP. The variability in BDOY was mainly determined by spring precipitation and the effective accumulated temperature, and the variability in EDOY was determined by autumn precipitation, SWC and LAI. NEP may decrease with declining precipitation in the future due to decreasing GEP, NEPmax, or CUP, and irrigation and residues cover may be useful in efforts to maintain current NEP levels. Our results indicate that interannual variability in NEP in agroecosystems may be more sensitive to changes in water conditions (such as precipitation, SWC and VPD) induced by climate changes, while temperature may be an important indirect factor when VPD is dominated.

Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models

Year-to-year variability in CO2 fluxes can yield insight into climate-carbon cycle relationships, a fundamental yet uncertain aspect of the terrestrial carbon cycle. In this study, we use global observations from NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite for years 2015–2019 and a geostatistical inverse model to evaluate 5 years of interannual variability (IAV) in CO2 fluxes and its relationships with environmental drivers. OCO-2 launched in late 2014, and we specifically evaluate IAV during the time period when OCO-2 observations are available. We then compare inferences from OCO-2 with state-of-the-art process-based models (terrestrial biosphere model, TBMs). Results from OCO-2 suggest that the tropical grasslands biome (including grasslands, savanna, and agricultural lands within the tropics) makes contributions to global IAV during the 5 year study period that are comparable to tropical forests, a result that differs from a majority of TBMs. Furthermore, existing studies disagree on the environmental variables that drive IAV during this time period, and the analysis using OCO-2 suggests that both temperature and precipitation make comparable contributions. TBMs, by contrast, tend to estimate larger IAV during this time and usually estimate larger relative contributions from the extra-tropics. With that said, TBMs show little consensus on both the magnitude and the contributions of different regions to IAV. We further find that TBMs show a wide range of responses on the relationships of CO2 fluxes with annual anomalies in temperature and precipitation, and these relationships across most of the TBMs have a larger magnitude than inferred from OCO-2. Overall, the findings of this study highlight large uncertainties in process-based estimates of IAV during recent years and provide an avenue for evaluating these processes against inferences from OCO-2.

Could phenological records from Chinese poems of the Tang and Song dynasties (618–1279 CE) be reliable evidence of past climate changes?

Abstract. Phenological records in historical documents have been proven to be of unique value for reconstructing past climate changes. As a literary genre, poetry reached its peak in the Tang and Song dynasties (618–1279 CE) in China. Sources from this period could provide abundant phenological records in the absence of phenological observations. However, the reliability of phenological records from poems, as well as their processing methods, remains to be comprehensively summarized and discussed. In this paper, after introducing the certainties and uncertainties of phenological information in poems, the key processing steps and methods for deriving phenological records from poems and using them in past climate change studies are discussed: (1) two principles, namely the principle of conservatism and the principle of personal experience, should be followed to reduce uncertainties; (2) the phenological records in poems need to be filtered according to the types of poems, background information, rhetorical devices, spatial representations, and human influence; (3) animals and plants are identified at the species level according to their modern distributions and the sequences of different phenophases; (4) phenophases in poems are identified on the basis of modern observation criteria; (5) the dates and sites for the phenophases in poems are confirmed from background information and related studies. As a case study, 86 phenological records from poems of the Tang Dynasty in the Guanzhong region in China were extracted to reconstruct annual temperature anomalies in specific years in the period between 600 and 900 CE. Following this, the reconstruction from poems was compared with relevant reconstructions in published studies to demonstrate the validity and reliability of phenological records from poems in studies of past climate changes. This paper reveals that the phenological records from poems could be useful evidence of past climate changes after being scientifically processed. This could provide an important reference for future studies in this domain, in both principle and methodology, pursuant of extracting and applying phenological records from poems for larger areas and different periods in Chinese history.

Spatio-temporal Variability and Trends in Rainfall and Temperature in Anger watershed, Southwestern Ethiopia

Insights to broadly argued research gap on lack of climate studies at micro-scale considering unique features of an area, this paper intended to examine agro-ecological level spatio-temporal trends and variability in rainfall and temperature in Anger watershed of southwestern Ethiopia. The gridded data managed by the Ethiopia National Meteorological Services Agency (NMSA) for 1983-2018 were used. The Mann-Kendall test for trend analysis and different variability measures were used. Questionnaire and FGD data on community perceptions gathered from 214 households and elders were analysed descriptively and qualitatively. The study reveals the consistent increasing trends in temperature; and high variability and insignificant but increasing rainfall trend. The trends and variability show spatio-temporal differences along agro-ecologies. The watershed is characterized by moderate to high rainfall coefficient of variations, significant years of high rainfall concentration, and considerable negative annual rainfall anomalies; that the variability was severe in woinadega followed by kolla agro-ecology. Although, the perceptions on trends, variability and its implications show difference across agro-ecology, the propensity to increased temperature, unclear rainfall trend and significant inter-annual and seasonal variability were witnessed. Unpredictability of rainfall time, concentrations in kiremt, and unexpected rain during harvesting was major challenges resulting multifaceted impacts on the small-scale farmers’ livelihoods.

Signatures of human intervention – or not? Downstream intensification of hydrological drought along a large Central Asian river: the individual roles of climate variability and land use change

Abstract. The transboundary Helmand River basin (HRB) is the main drainage system for large parts of Afghanistan and the Sistan region of Iran. Due to the reliance of this arid region on water from the Helmand River, a better understanding of hydrological-drought pattern and the underlying drivers in the region is critically required for effective management of the available water. The objective of this paper is therefore to analyze and quantify spatiotemporal pattern of drought and the underlying processes in the study region. More specifically we test for the Helmand River basin the following hypotheses for the 1970–2006 period: (1) drought characteristics, including frequency and severity, systematically changed over the study period; (2) the spatial pattern and processes of drought propagation through the Helmand River basin also changed; and (3) the relative roles of climate variability and human influence on changes in hydrological droughts can be quantified. It was found that drought characteristics varied throughout the study period but largely showed no systematic trends. The same was observed for the time series of drought indices SPI (standard precipitation index) and SPEI (standardized precipitation evapotranspiration index), which exhibited considerable spatial coherence and synchronicity throughout the basin, indicating that, overall, droughts similarly affect the entire HRB with few regional or local differences. In contrast, analysis of the SDI (streamflow drought index) exhibited significant negative trends in the lower parts of the basin, indicating an intensification of hydrological droughts. It could be shown that with a mean annual precipitation of ∼ 250 mm yr−1, streamflow deficits and thus hydrological drought throughout the HRB are largely controlled by precipitation deficits, whose annual anomalies on average account for ±50 mm yr−1, or ∼ 20 % of the water balance of the HRB, while anomalies of total evaporative fluxes on average only account for ±20 mm yr−1. Assuming no changes in the reservoir management practices over the study period, the results suggest that the two reservoirs in the HRB only played a minor role for the downstream propagation of streamflow deficits, as indicated by the mean difference between inflow and outflow during drought periods, which did not exceed ∼ 0.5 % of the water balance of the HRB. Irrigation water abstraction had a similarly limited effect on the magnitude of streamflow deficits, accounting for ∼ 10 % of the water balance of the HRB. However, the downstream parts of the HRB moderated the further propagation of streamflow deficits and associated droughts because of the minor effects of reservoir operation and very limited agricultural water in the early decades of the study period. This drought moderation function of the lower basin was gradually and systematically inverted by the end of the study period, when the lower basin eventually amplified the downstream propagation of flow deficits and droughts. Our results provide plausible evidence that this shift from drought moderation to drought amplification in the lower basin is likely a consequence of increased agricultural activity and the associated increases in irrigation water demand, from ∼ 13 mm yr−1 at the beginning of the study period to ∼ 23 mm yr−1 at the end, and thus in spite of being only a minor fraction of the water balance. Overall the results of this study illustrate that flow deficits and the associated droughts in the HRB clearly reflect the dynamic interplay between temporally varying regional differences in hydro-meteorological variables together with subtle and temporally varying effects linked to direct human intervention.

Variability of Surface Radiation Budget Components Over the U.S. From 1996 to 2019—Has Brightening Ceased?

AbstractThe record of downwelling solar irradiance and other surface radiation budget components for the U.S. has been extended through 2019 using SURFRAD Network data. Brightening of surface solar irradiance of +7.36 Wm−2/decade occurred from 1996 through 2012. In 2013, surface solar radiation sharply decreased to the long‐term mean (representing 1996–2019) and remained near that level through 2017. Successive decreases in 2018 and 2019 yielded a dimming trend of −3.90 Wm−2/decade after 2012, but with a high uncertainty owing to the observed variability and brief period covered. Individually, all stations but Penn State showed brightening trends consistent with the network average, and surface solar irradiance decreased at all stations after 2012. Total surface net radiation showed similar tendencies but the reversal from increasing to decreasing was more gradual because of the response of surface net longwave to the changing solar input. Aerosol optical depth decreased continuously throughout the tenure of the network but accounted for only 3% of the variability of surface solar irradiance, while cloud fraction explained 62%. The mean cloud fraction was 2.4% greater during the dimming period than the brightening period but showed no trends due to high interannual variability. However, annual anomalies of direct‐normal solar radiation, which relate to sun duration and clouds, generally increased to 2012 and then decreased thereafter. Collectively, these results indicate that changing cloud cover was the primary source of brightening and dimming over the U.S. from 1996 to 2019.

Changing Patterns of Temperature in Yobe State, North-Eastern Nigeria: An Evidence of Climate Change

Air temperature is one of the fundamental indicators of Climate Change in any place on earth. Understanding the changing patterns of air temperature in Yobe State is instrumental in establishing the manifestation of Climate Change in the area and to enables the policy makers to design the best mechanisms to cope with the vagaries of increasing temperature. This study examined the changing patterns of temperature in Yobe State as an evidence of Climate Change. The study used mean monthly minimum and maximum air temperature data collected from the archives of the Nigerian Meteorological Agency (NIMET) with respect to Nguru and Potiskum weather stations in the State for a period of 1956-2015. The data was analyzed using descriptive statistics, linear regression trend lines and coefficient of variation (C.V). The result showed that April is the hottest month with the highest mean maximum temperatures of 39.6°C and 38.6°C in Nguru and Potiskum respectively while January is the coldest month with mean temperatures of 30.5°C and 30.8°C for Nguru and Potiskum respectively. Further finding revealed that temperatures are highly variable in February in both Nguru and Potiskum with C.V of 8.7% and 5.7% and less variable in October with C.V of 2.0%. Finding showed that in general term, there were upward trends in monthly and annual positive temperature anomalies ranging from 0.2°C to as high as 4.0°C in some months and years in the state. The study concludes that temperatures in Yobe State have witnessed series of changes within the 60 years period. Therefore, it is suggested that stakeholders in the Yobe State Environmental Protection Agency should intensify efforts in reducing deforestation while encouraging people on afforestation with a view to reducing the impact of Greenhouse gases that are contributing to more warming than normal. There should be adequate electricity and water supply for immediate relieve from excessive heat emanating from high temperature.

Zooplankton variability in the Strait of Georgia, Canada, and relationships with the marine survivals of Chinook and Coho salmon.

The Strait of Georgia, Canada, has complex interactions among natural and human pressures that confound understanding of changes in this system. We report on the interannual variability in biomass of 12 zooplankton taxonomic groups in the deep (bottom depths greater than 50 m) central and northern Strait of Georgia from 1996 to 2018, and their relationships with 10 physical variables. Total zooplankton biomass was dominated (76%) by large-sized crustaceans (euphausiids, large and medium size calanoid copepods, amphipods). The annual anomaly of total zooplankton biomass was highest in the late 1990s, lowest in the mid-2000s, and generally above its climatological (1996-2010) average after 2011, although many individual groups had different patterns. Two latent trends (derived from dynamic factor analyses) described the variability of annual biomass anomalies underlying all zooplankton groups: a U-shaped trend with its minimum in the mid-2000s, and a declining trend from 2001 to 2011. Two latent trends also described the physical variables. The variability represented by these four latent trends clustered into two periods: 1996-2006, with generally declining zooplankton biomass and increasing salinities, and 2007-2018, with increasing zooplankton biomass and decreasing salinities. ARIMA modelling showed sea surface salinity at Entrance Island in the middle Strait of Georgia, the Pacific Decadal Oscillation, and the peak date of the spring phytoplankton bloom were significantly related to the two latent zooplankton trends. ARIMA models comparing zooplankton and physical variables with the marine survivals of four salmon populations which enter the Strait as juveniles (Chinook: Cowichan River, Puntledge River, Harrison River; Coho: Big Qualicum River) all included zooplankton groups consistent with known salmon prey; prominent among the physical variables were sea surface salinity and variables representing the flow from the Fraser River. These regressions explained (adjR2) 38 to 85% of the annual variability in marine survival rates of these salmon populations over the study time period. Although sea temperature was important in some relationships between zooplankton biomass and salmon marine survival, salinity was a more frequent and more important variable, consistent with its influence on the hydrodynamics of the Strait of Georgia system.

Winter warming detection using temperature and precipitation anomalies in arid and semi-arid areas

Abstract Extreme winter warming can affect many aspects of environmental and human related activities. It can be disastrous, especially in arid regions. However, no specific research has been carried out on detecting winter warming in Iran. To address this research gap, this study was performed to investigate winter warming in the arid and semi-arid areas located in northeastern Iran. For this purpose, anomalies of minimum and maximum daily temperature, average daily temperature, mean daily temperature range and mean daily precipitation were studied on monthly, seasonal, annual and decadal scales. Along with this, the trend in the data was analyzed using the Mann–Kendall (MK) test. The results showed that since the 1990s there has been a significant increase in temperature positive anomalies at most stations. In addition, the precipitation anomaly mutations occurred later than temperature. In most cases the increase in winter anomalies was higher than the average annual anomalies. As an example, the maximum winter temperature anomaly increased from 0.38 °C in the 1990s to 2.07 °C in the 2000s at Mashhad station. Due to the simultaneous increase in anomalies at most stations, the detected winter warming is more likely to be the result of global warming rather than local synoptic climate.

The impact of timing and magnitude of the El Niño- Southern Oscillation on local precipitation levels and temperatures in the Bay Area

In this study, we analyzed temperature, Multivariate El Niño-Southern Oscillation Index (MEI), and Standard Precipitation Index (SPI) data from the San Francisco Bay Area from 1971 to 2016. We also analyzed CO2 records from Mauna Loa, HI for the same time period, along with the annual temperature anomalies for the Bay Area. Understanding the relationships between temperature, MEI, SPI, and CO2 concentration is important as they measure the major influencers of California’s regional climate: temperature, ENSO, precipitation, and atmospheric CO2. Thus, measurements of the three variables are key indicators of long term trends in climate, and can reveal the exact effect anthropogenic climate change is having on the Bay Area’s climate. Our research question was whether there is a correlation between temperature, MEI, SPI, and atmospheric CO2 within the Bay Area. We found that there was a clear correlation between warm anomalies and high MEI/low SPI in the period of 2013–2016, however only when both were historically significant. Also, MEI levels in general were highly correlated with temperature, showing that the local temperature anomalies in the Bay Area are significantly influenced by the El Niño-Southern Oscillation (ENSO) cycle. The influence of precipitation on the local temperature anomalies was limited, however. Finally, although there was not a statistically significant link between the atmospheric CO2 concentration at Mauna Loa, HI and the temperature anomalies in the Bay Area, the consistent increase in CO2 concentration could have had an impact on the overall increase in annual temperature anomalies from 1971 to 2016.

Temporal studies of climatic and environmental parameters and their possible impact on selected reservoir fish production of Madhya Pradesh: A different methodological approach

Climate change has a significant influence on global biodiversity and would like to affect the structure and function of natural ecosystems. This study assessed the quantitative impact of climatic change on the selected reservoirs. The climatic data of Madhya Pradesh were collected from the Indian Meteorological Department (IMD). The temperature and rainfall data under the A1B scenario were processed using Linux-based Ferret software, whereas chlorophyll-a and water surface temperature data were retrieved from Landsat-8 imagery using SNAP and ARC GIS software. Annual Anomaly analysis for average temperature and rainfall found, warming in annual temperature at Indirasagar reservoir under A1B scenarios during 1991–2015, 2016–2040 and 2041–2065 while, rainfall decreased between 1991–2015 and 2016–2040 but would like to be increased during (20412065). Similar results were found in the case of the Gandhisagar reservoir as well. Seasonal trend analysis was done using a statistical non-parametric test, Mann-Kendall (M-K) test, which showed an increasing trend for average temperature in post-monsoon and Winter, while the decreasing trend was found in Monsoon and Pre-Monsoon in Indirasagar reservoir. This study also found a significant decreasing trend for rainfall in all three seasons except Monsoon. In the case of Gandhisagar reservoir, an increasing trend has found on average temperature for all the seasons while rainfall showed a significantly decreasing trend in all the seasons except Post-Monsoon. This study also predicted the reservoir fish production using different prediction models and Auto-Regressive Integrated Moving Average with exogenous variables (ARIMAX) model found as the most suitable model in case of forecasting. It was also found that mean maximum temperature, mean minimum temperature and mean wind speed were the most significant variables in predicting fish production.

Nutrient supply affects the yield stability of major European crops—a 50 year study

Yield stability is important for food security and a sustainable crop production, especially under changing climatic conditions. It is well known that the variability of yields is linked to changes in meteorological conditions. However, little is known about the long-term effects of agronomic management strategies, such as the supply of important nutrients. We analysed the stability of four major European crops grown between 1955 and 2008 at a long-term fertilization experiment located in Germany. Six fertilizer treatments ranged from no fertilization over the omission of individual macronutrients to complete mineral fertilization with all major macronutrients (nitrogen, phosphorus, potassium and calcium). Yield stability was estimated for each crop × treatment combination using the relative yield deviation in each year from the corresponding (nonlinear) trend value (relative yield anomalies (RYA)). Stability was lowest for potato, followed by sugar beet and winter wheat and highest for winter rye. Stability was highest when soils had received all nutrients with the standard deviation of RYA being two to three times lower than for unfertilized plots. The omission of nitrogen and potassium was associated with a decrease in yield stability and a decrease in the number of simultaneous positive and negative yield anomalies among treatments. Especially in root crops nutrient supply strongly influenced both annual yield anomalies and changes in anomalies over time. During the second half of the observation period yield stability decreased for sugar beet and increased for winter wheat. Potato yields were more stable during the second period, but only under complete nutrient supply. The critical role of potassium supply for yield stability suggests potential links to changes in the water balance during the last decades. Results demonstrate the need to explicitly consider the response of crops to long-term nutrient supply for understanding and predicting changes in yield stability.

A global mean sea surface temperature dataset for the Last Interglacial (129–116 ka) and contribution of thermal expansion to sea level change

Abstract. A valuable analogue for assessing Earth's sensitivity to warming is the Last Interglacial (LIG; 129–116 ka), when global temperatures (0 to +2 ∘C) and mean sea level (+6 to 11 m) were higher than today. The direct contribution of warmer conditions to global sea level (thermosteric) is uncertain. We report here a global network of LIG sea surface temperatures (SST) obtained from various published temperature proxies (e.g. faunal and floral plankton assemblages, Mg ∕ Ca ratios of calcareous organisms, and alkenone U37K′). We summarize the current limitations of SST reconstructions for the LIG and the spatial temperature features of a naturally warmer world. Because of local δ18O seawater changes, uncertainty in the age models of marine cores, and differences in sampling resolution and/or sedimentation rates, the reconstructions are restricted to mean conditions. To avoid bias towards individual LIG SSTs based on only a single (and potentially erroneous) measurement or a single interpolated data point, here we report average values across the entire LIG. Each site reconstruction is given as an anomaly relative to 1981–2010, corrected for ocean drift, and where available seasonal estimates are provided (189 annual, 99 December–February, and 92 June–August records). To investigate the sensitivity of the reconstruction to high temperatures, we also report maximum values during the first 5 millennia of the LIG (129–124 ka). We find mean global annual SST anomalies of 0.2 ± 0.1 ∘C averaged across the LIG and an early maximum peak of 0.9 ± 0.1 ∘C, respectively. The global dataset provides a remarkably coherent pattern of higher SST increases at polar latitudes than in the tropics (demonstrating the polar amplification of surface temperatures during the LIG), with comparable estimates between different proxies. Polewards of 45∘ latitude, we observe annual SST anomalies averaged across the full LIG of > 0.8 ± 0.3 ∘C in both hemispheres with an early maximum peak of > 2.1 ± 0.3 ∘C. Using the reconstructed SSTs suggests a mean LIG global thermosteric sea level rise of 0.08 ± 0.1 m and a peak contribution of 0.39 ± 0.1 m, respectively (assuming warming penetrated to 2000 m depth). The data provide an important natural baseline for a warmer world, constraining the contributions of Greenland and Antarctic ice sheets to global sea level during a geographically widespread expression of high sea level, and can be used to test the next inter-comparison of models for projecting future climate change. The dataset described in this paper, including summary temperature and thermosteric sea level reconstructions, is available at https://doi.org/10.1594/PANGAEA.904381 (Turney et al., 2019).

Resilience of an Integrated Crop–Livestock System to Climate Change: A Simulation Analysis of Cover Crop Grazing in Southern Brazil

Integrated crop–livestock systems are a form of sustainable intensification of agriculture that rely on synergistic relationships between plant and animal system elements to bolster critical agroecosystem processes, with potential impacts on resilience to weather anomalies. We simulated productivity dynamics in an integrated cover crop grazing agroecosystem typical of southern Brazil to gain a better understanding of the impacts of livestock integration on system performance, including future productivity and resilience under climate change. Long-term historical simulations in APSIM showed that the integrated system resulted in greater system-wide productivity than a specialized control system in 77% of simulated years. Although soybean yields were typically lower in the integrated system, the additional forage and livestock production increased total system outputs. Under simulated future climate conditions [representative concentration pathway 8.5 (RCP8.5) scenario from 2020 to 2060], integrated system productivity exceeded specialized system productivity in 95% of years despite declines in average soybean yield and aboveground cover crop biomass production. While the integrated system provided a productivity buffer against chronic climate stress, its resilience to annual weather anomalies depended on disturbance type and timing. This study demonstrates the utility of process-based models for exploring biophysical proxies for resilience, as well as the potential advantages of livestock integration into cropland as a sustainable intensification strategy.

A climatic dipole drives short- and long-term patterns of postfire forest recovery in the western United States

Researchers are increasingly examining patterns and drivers of postfire forest recovery amid growing concern that climate change and intensifying fires will trigger ecosystem transformations. Diminished seed availability and postfire drought have emerged as key constraints on conifer recruitment. However, the spatial and temporal extent to which recurring modes of climatic variability shape patterns of postfire recovery remain largely unexplored. Here, we identify a north-south dipole in annual climatic moisture deficit anomalies across the Interior West of the US and characterize its influence on forest recovery from fire. We use annually resolved establishment models from dendrochronological records to correlate this climatic dipole with short-term postfire juvenile recruitment. We also examine longer-term recovery trajectories using Forest Inventory and Analysis data from 989 burned plots. We show that annual postfire ponderosa pine recruitment probabilities in the northern Rocky Mountains (NR) and the southwestern US (SW) track the strength of the dipole, while declining overall due to increasing aridity. This indicates that divergent recovery trajectories may be triggered concurrently across large spatial scales: favorable conditions in the SW can correspond to drought in the NR that inhibits ponderosa pine establishment, and vice versa. The imprint of this climatic dipole is manifest for years postfire, as evidenced by dampened long-term likelihoods of juvenile ponderosa pine presence in areas that experienced postfire drought. These findings underscore the importance of climatic variability at multiple spatiotemporal scales in driving cross-regional patterns of forest recovery and have implications for understanding ecosystem transformations and species range dynamics under global change.

Biomass burning spatiotemporal variations over South and Southeast Asia

In this study, Moderate Resolution Imaging Spectroradiometer active fire and land use products were integrated to extract and classify biomass burning (BB) data for South Asia (SA) and Southeast Asia (SEA). Several trend and geographic distribution analyses were conducted at the grid (0.25° × 0.25°) and regional scales. As the principal local form of BB, crop residue burning (CRB) in SA increased by 844 spots/yr, and the Mann–Kendall (MK) τ reached 0.61. Additionally, the CRB in Punjab-Haryana, a region a well-known for severest CRB, presented a significant declining trend. BB in mainland SEA was much more intense and was dominated by forest and shrubland fires. Forest fires in mainland SEA declined at a rate of −209 spots/yr, and shrubland fire conversely grew at a rate of 803 spots/yr, which was likely related to the dramatic land cover change induced by the local swidden agriculture. Unlike other regions, BB in equatorial SEA primarily occurred in the second half of the year (August to October), and it was extremely vulnerable to El Niño events. When the annual sea surface temperature anomalies within the Niño 3 region improved by 1 °C, the annual BB spots and fire radiative power in equatorial SEA increased by 5.18 × 104 and 2.40 × 106 MW, respectively. Although the interannual variations in equatorial SEA were dramatic, the robust Siegel’s repeated median estimator still revealed that equatorial SEA BB significantly declined by −1825 spots/yr. This regional decline reflects government endeavors to curb indigenous BB. However, regions with enhanced BB still need to draw more attention, and it is imperative for the Indonesian government to take substantial measures to reduce anthropogenic fire sources during El Niño events.

Village-level climate and weather variability, mediated by village-level crop yield, is associated with linear growth in children in Uganda

IntroductionTo investigate total annual precipitation, precipitation anomaly and aridity index in relation to linear growth in children under 5 in Uganda and quantify the mediating role of crop yield.MethodsWe analysed...

Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017)

The study assesses the long-term spatial and temporal solar resource variability in America using the 20-year National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB). The coefficient of variation (COV) is used to analyze the spatial and temporal (interannual and seasonal) variability. Further, both spatial and temporal long-term variability are analyzed using the Köppen-Geiger climate classification. The temporal variability is found that, on average, the continental United States (CONUS) COV reaches up to 5% for global horizontal irradiance (GHI) and 10% for direct normal irradiance (DNI), and that the NSRDB domain's COV is roughly twice that of CONUS. For the seasonal variability analysis, the winter months are found to exhibit higher COV than the other seasons. In particular, December exhibits the highest variability, reaching on average 30% for DNI and 20% for GHI over various areas. On the other hand, the summer months demonstrate significantly lower variability, reaching only less than 20% for DNI and 10% for GHI, on average. Similarly, the spatial variability is analyzed by comparing each pixel to its neighbors. The long-term spatial variability is found to increase with the number of neighboring pixels being considered, which is equivalent to an increase in distance (within a 100-km x 100-km square grid). As expected, the DNI spatial variability is higher than that of GHI. Moreover, the annual solar irradiance anomalies are found to reach ±25% for both GHI and DNI (and even exceed those value in some instances) during each year of the 20-year period.

Assessing the Influence of Tourism-Driven Activities on Environmental Variables on Hainan Island, China

Tourism is a primary socio-economic factor on many coastal islands. Tourism contributes to the livelihoods of the residents, but also influences natural resources and energy consumption and can become a significant driver of land conversion and environmental change. Understanding the influence of tourist-related activities is vital for sustainable tourism development. We chose Hainan Island in South China as a research area to study the influence of tourist-driven activities on environmental variables (as Land Surface Temperatures (LST) and related ecosystem variables) during the period of 2000 to 2019. In Hainan, the local economy relies heavily on tourism, with an ever-growing influx of tourists each year. We categorised location-based points of interest (POIs) into two classes, non-tourism sites and tourism-related sites, and utilised satellite data from the cloud-based platform Google Earth Engine (GEE) to extract LST and Normalized Difference Vegetation Index (NDVI) data. We analysed the LST variations, NDVI changes and the land use/land cover (LULC) changes and compared the relative difference in LST and NDVI between the tourism-related sites and non-tourism-related sites. The main findings of this study were: (1) The median LST in the tourism-related sites was relatively higher (1.3) than the LST in the non-tourism-related sites for the 20 years. Moreover, every annual mean LST of tourism-related sites was higher than the LST values in non-tourism-related sites, with an average difference of 1.2 °C for the 20 years and a maximum difference of 1.7 °C. We found higher annual LST anomalies for tourist-related sites compared to non-tourism sites after 2010, which indicated the likely positive differences in LST above the average LST during 20 years for tourism-related sites when compared against the non-tourism related sites, thus highlighting the potential influence of tourism activities on LST. (2) The annual mean NDVI value for tourism-related sites was significantly lower than for non-tourism places every year, with an average NDVI difference of 0.26 between the two sites. (3) The land cover changed significantly: croplands and forests reduced by 3.5% and 2.8% respectively, while the areas covered by orchards and urban areas increased by 2% and 72.3% respectively. These results indicate the influence of the tourism-driven activities includes the relatively high LST, vegetation degradation and land-use conversion particular to urban cover type. The outcome of this work provides a method that combines cloud-based satellite-derived data with location-based POIs data for quantifying the long-term influence of tourism-related activities on sensitive coastal ecosystems. It contributes to designing evidence-driven management plans and policies for the sustainable tourism development in coastal areas.

Climate–groundwater dynamics inferred from GRACE and the role of hydraulic memory

Abstract. Groundwater is the largest store of freshwater on Earth after the cryosphere and provides a substantial proportion of the water used for domestic, irrigation and industrial purposes. Knowledge of this essential resource remains incomplete, in part, because of observational challenges of scale and accessibility. Here we examine a 14-year period (2002–2016) of Gravity Recovery and Climate Experiment (GRACE) observations to investigate climate–groundwater dynamics of 14 tropical and sub-tropical aquifers selected from WHYMAP's (Worldwide Hydrogeological Mapping and Assessment Programme) 37 large aquifer systems of the world. GRACE-derived changes in groundwater storage resolved using GRACE Jet Propulsion Laboratory (JPL) mascons and the Community Land Model's land surface model are related to precipitation time series and regional-scale hydrogeology. We show that aquifers in dryland environments exhibit long-term hydraulic memory through a strong correlation between groundwater storage changes and annual precipitation anomalies integrated over the time series; aquifers in humid environments show short-term memory through strong correlation with monthly precipitation. This classification is consistent with estimates of groundwater response times calculated from the hydrogeological properties of each system, with long (short) hydraulic memory associated with slow (rapid) response times. The results suggest that groundwater systems in dryland environments may be less sensitive to seasonal climate variability but vulnerable to long-term trends from which they will be slow to recover. In contrast, aquifers in humid regions may be more sensitive to climate disturbances such as drought related to the El Niño–Southern Oscillation but may also be relatively quick to recover. Exceptions to this general pattern are traced to human interventions through groundwater abstraction. Hydraulic memory is an important factor in the management of groundwater resources, particularly under climate change.