Interannual Trends Research Articles

Extreme Aerosol Events at Mesa Verde, Colorado: Implications for Air Quality Management

A significant concern for public health and visibility is airborne particulate matter, especially during extreme events. Of most relevance for health, air quality, and climate is the role of fine aerosol particles, specifically particulate matter with aerodynamic diameters less than or equal to 2.5 micrometers (PM2.5). The purpose of this study was to examine PM2.5 extreme events between 1989 and 2018 at Mesa Verde, Colorado using Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring data. Extreme events were identified as those with PM2.5 on a given day exceeding the 90th percentile value for that given month. We examine the weekly, monthly, and interannual trends in the number of extreme events at Mesa Verde, in addition to identifying the sources of the extreme events with the aid of the Navy Aerosol Analysis and Prediction (NAAPS) aerosol model. Four sources were used in the classification scheme: Asian dust, non-Asian dust, smoke, and “other”. Our results show that extreme PM2.5 events in the spring are driven mostly by the dust categories, whereas summertime events are influenced largely by smoke. The colder winter months have more influence from “other” sources that are thought to be largely anthropogenic in nature. No weekly cycle was observed for the number of events due to each source; however, interannual analysis shows that the relative amount of dust and smoke events compared to “other” events have increased in the last decade, especially smoke since 2008. The results of this work indicate that, to minimize and mitigate the effects of extreme PM2.5 events in the southwestern Colorado area, it is important to focus mainly on smoke and dust forecasting in the spring and summer months. Wintertime extreme events may be easier to regulate as they derive more from anthropogenic pollutants accumulating in shallow boundary layers in stagnant conditions.

Four Decades of Surface Temperature, Precipitation, and Wind Speed Trends over Lakes of Greece

Climate change is known to affect world’s lakes in many ways. Lake warming is perhaps the most prominent impact of climate change but there is evidence that changes of precipitation and wind speed over the surface of the lakes may also have a significant effect on key limnological processes. With this study we explored the interannual trends of surface temperature, precipitation, and wind speed over 18 lakes of Greece using ERA5-Land data spanning over a period of almost four decades. We used generalized additive models (GAMs) to conduct time-series analysis in order to identify significant trends of change. Our results showed that surface temperature has significantly increased in all lakes with an average rate of change for annual temperature of 0.43 °C decade−1. With regard to precipitation, we identified significant trends for most lakes and particularly we found that precipitation decreased during the first two decades (1981–2000), but since 2000 it increased notably. Finally, wind speed did not show any significant change over the examined period with the exception for one lake. In summary, our work highlights the major climatic changes that have occurred in several freshwater bodies of Greece. Thus, it improves our understanding on how climate change may have impacted the ecology of these important ecosystems and may aid us to identify systems that are more vulnerable to future changes.

Impact of Thermohaline Variability on Sea Level Changes in the Southern Ocean

AbstractThe Southern Ocean is responsible for the majority of the global oceanic heat uptake that contributes to global sea level rise. At the same time, ocean temperatures do not change at the same rate in all regions and sea level variability is also affected by changes in salinity. This study investigates 10 years of steric height variability (2008–2017) in the Southern Ocean (30°S to 70°S) by analyzing temperature and salinity variations obtained from the GLORYS‐031 model provided by the European Copernicus Marine Environment Monitoring Service. The thermohaline variability is decomposed into thermohaline modes using a functional Principal Component Analysis. Thermohaline modes provide a natural basis to decompose the joint temperature‐salinity vertical profiles into a sum of vertical modes weighted by their respective principal components that can be related to steric height. Interannual steric height trends are found to differ significantly between subtropical and subpolar regions, simultaneously with a shift from a thermohaline stratification dominated by the first “thermal” mode in the north to the second ’saline’ mode in the South. The Polar Front appears as a natural boundary between the two regions, where steric height variations are minimized. Despite higher melt rates and atmospheric temperatures, steric height in Antarctic waters (0–2,000 m) has dropped since 2008 due to higher salt content in the surface and upper intermediate layer and partially colder waters, while subtropical waters farther north have mostly risen due to increased heat storage.

Multi-scale trend analysis of water quality using error propagation of generalized additive models

Effective stewardship of ecosystems to sustain current ecological status or mitigate impacts requires nuanced understanding of how conditions have changed over time in response to anthropogenic pressures and natural variability. Detecting and appropriately characterizing changes requires accurate and flexible trend assessment methods that can be readily applied to environmental monitoring datasets. A key requirement is complete propagation of uncertainty through the analysis. However, this is difficult when there are mismatches between sampling frequency, period of record, and trends of interest. Here, we propose a novel application of generalized additive models (GAMs) for characterizing multi-decadal changes in water quality indicators and demonstrate its utility by analyzing a 30-year record of biweekly-to-monthly chlorophyll-a concentrations in the San Francisco Estuary. GAMs have shown promise in water quality trend analysis to separate long-term (i.e., annual or decadal) trends from seasonal variation. Our proposed methods estimate seasonal averages in a response variable with GAMs, extract uncertainty measures for the seasonal estimates, and then use the uncertainty measures with mixed-effects meta-analysis regression to quantify inter-annual trends that account for full propagation of error across methods. We first demonstrate that nearly identical descriptions of temporal changes can be obtained using different smoothing spline formulations of the original time series. We then extract seasonal averages and their standard errors for an a priori time period within each year from the GAM results. Finally, we demonstrate how across-year trends in seasonal averages can be modeled with mixed-effects meta-analysis regression that propagates uncertainties from the GAM fits to the across-year analysis. Overall, this approach leverages GAMs to smooth data with missing observations or varying sample effort across years to estimate seasonal averages and meta-analysis to estimate trends across years. Methods are provided in the wqtrends R package.

Trends and Variability of North American Cool-Season Extratropical Cyclones: 1979–2019

AbstractExtratropical cyclones are the primary driver of sensible weather conditions across the mid-latitudes of North America, often generating various types of precipitation, gusty non-convective winds, and severe convective storms throughout portions of the annual cycle. Given ongoing modifications of the zonal atmospheric thermal gradient due to anthropogenic forcing, analyzing the historical characteristics of these systems presents an important research question. Using the North American Regional Reanalysis, boreal cool-season (October–April) extratropical cyclones for the period 1979–2019 were identified, tracked, and classified based on their genesis location. Additionally, bomb cyclones—extratropical cyclones that recorded a latitude normalized pressure fall of 24 hPa in 24-hr—were identified and stratified for additional analysis. Cyclone lifespan across the domain exhibits a log-linear relationship, with 99% of all cyclones tracked lasting less than 8 days. On average, ≈ 270 cyclones were tracked across the analysis domain per year, with an average of ≈ 18 year−1 being classified as bomb cyclones. The average number of cyclones in the analysis domain has decreased in the last 20 years from 290 year−1 during the period 1979–1999 to 250 year−1 during the period 2000–2019. Spatially, decreasing trends in the frequency of cyclone track counts were noted across a majority of the analysis domain, with the most significant decreases found in Canada’s Northwest Territories, Colorado, and east of the Graah mountain range. No significant interannual or spatial trends were noted with bomb cyclone frequency.

Glacier surface velocities in the Jankar Chhu Watershed, western Himalaya, India: Study using Landsat time series data (1992–2020)

This study presents a spatial and temporal surface velocity analysis of 33 glaciers in the Jankar Chhu Watershed (JCW), western Himalaya. The surface velocity was computed using the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) tool on the Landsat images spanning nearly three decades (1992–2020). Long-term (1948–2018) inter-annual and seasonal climatic trends are also evaluated based on the US NCEP/NCAR reanalyzed gridded data. The role of several topographic and climatic parameters was examined to characterize heterogeneity in velocity.Glaciers in the JCW exhibit three distinct spatial velocity patterns. The type-I glaciers show nearly stagnant (<5 m yrˉ1) debris-covered tongues but a maximum velocity of >60 m yrˉ1 in the debris-free accumulation area. The type-II glaciers exhibit active tongues (>50 m yrˉ1) yet have slow movement in the accumulation zone. The type-III glaciers show prolonged (<10 m yrˉ1) yet consistent movement throughout the flow line. Results show that the average surface velocity of studied glaciers was 11.10 ± 4.1 m yrˉ1 in 1992/93, which increased (by ~43%) to 15.87 ± 2.4 m yrˉ1 in 2000/02 and further increased (by ~59%) to 25.25 ± 1.5 m yrˉ1 in 2017/18. Glaciers show seasonal fluctuations in surface velocities with an apparent “summer speedup” of ~19%–200% compared to the winter velocities. Inter-annual velocities show an increasing trend during the observation periods. Analysis of NCEP/NCAR gridded data also reveals that the temperature increased by 0.55 °C while precipitation decreased by 0.73 mm during 1948–2018.Our results indicate that glaciers remain dynamic throughout their length with systematic spatiotemporal variations in surface velocity that points towards the control of several topographical and morphological factors. The spatially heterogeneous pattern in surface velocity is characterized by zones of high activity crevasses with steep gradients (>30°) showing maximum ice movement at higher elevations, while debris-covered tongues in the low elevations having gentle slopes (<2°) are nearly stagnant. Based on our results and evidences from the literatures, we suggest that seasonal variations (i.e., summer speedup) in surface velocity in the JCW are due to seasonal temperature fluctuations that alter meltwater availability to the subglacial processes akin to the other Himalaya glaciers. An increasing trend in annual velocity manifests the rising trend in temperature in the western Himalaya. Local topographic configuration, combined with regional climatic systems, controls the amount and types of precipitation received in areas like ours to sustain glaciers on a millennial-scale.

Spatiotemporal variability of land surface temperature in north-western Ethiopia.

The aggravating deforestation, industrialization, and urbanization are becoming the principal causes for environmental challenges worldwide. As a result, satellite-based remote sensing helps to explore the environmental challenges spatially and temporally. This investigation analyzed the spatiotemporal variability in land surface temperature (LST) and its link with elevation in the Amhara region, Ethiopia. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (2001-2020) were used. The pixel-based linear regression model was used to explore the spatiotemporal variability of LST changes. Furthermore, Sen's slope and Mann-Kendall trend test were used to determine the magnitude of temporal shifts of the areal average LST and evaluate trends in areal average LST, respectively. Coefficient of variation (CV) was also used to analyze spatial and temporal variability in seasonal and annual LST. The seasonal LST CV varied from 1.096-10.72%, 0.7-11.06%, 1.29-14.76%, and 2.19-10.35% for average autumn (September to November), summer (June to August), spring (March to May), and winter (December to February) seasons, respectively. The highest inter-annual variability was observed in the eastern, northern, and south-western districts than that in the other parts. The seasonal spatial LST trend varied from -0.7-0.16, -0.4-0.224, 0.6-0.19, and -0.6-0.32 for average autumn, summer, spring, and winter seasons, respectively. Besides, the annual spatial LST slope varied from -0.58 to 0.17. Negative slopes were found in the central, mid-western, and mid-northern districts in annual LST, unlike the other parts. The annual variations of mean areal LST decreased insignificantly at the rate of 0.046°C year-1 (P<0.05). However, the inter-annual variability trend of annual LST increased significantly. Generally, the LST is tremendously variable in space and time and negatively correlated with elevation.

Intensified Moisture Sources of Heavy Precipitation Events Contributed to Interannual Trend in Precipitation Over the Three-Rivers-Headwater Region in China

Evidence has indicated an overall wetting trend over the Three-Rivers Headwater Region (TRHR) in the recent decades, whereas the possible mechanisms for this change remain unclear. Detecting the main moisture source regions of the water vapor and its increasing trend over this region could help understand the long-term precipitation change. Based on the gauge-based precipitation observation analysis, we find that the heavy precipitation events act as the main contributor to the interannual increasing trend of summer precipitation over the TRHR. A Lagrangian moisture tracking methodology is then utilized to identify the main moisture source of water vapor over the target region for the boreal summer period of 1980–2017, with focus particularly on exploring its change associated with the interannual trend of precipitation. On an average, the moisture sources for the target regions cover vast regions, including the west and northwest of the Tibetan Plateau by the westerlies, the southwest by the Indian summer monsoon, and the adjacent regions associated with the local recycling. However, the increased interannual precipitation trend over the TRHR could be largely attributed to the enhanced moisture sources from the neighboring northeastern areas of the targeted region, particularly associated with the heavy precipitation events. The increased water vapor transport from the neighboring areas of the TRHR potentially related to the enhanced local hydrological recycling over these regions plays a first leading role in the recent precipitation increase over the TRHR.

Spatial, Phenological, and Inter-Annual Variations of Gross Primary Productivity in the Arctic from 2001 to 2019

Quantifying the spatial, seasonal (phenological), and inter-annual variations of gross primary productivity (GPP) in the Arctic is critical for comprehending the terrestrial carbon cycle and its feedback to climate warming in this region. Here, we evaluated the accuracy of the MOD17A2H GPP product using the FLUXNET 2015 dataset in the Arctic, then explored the spatial patterns, seasonal variations, and interannual trends of GPP, and investigated the dependence of the spatiotemporal variations in GPP on land cover types, latitude, and elevation from 2001 to 2019. The results showed that MOD17A2H was consistent with in situ measurements (R = 0.8, RMSE = 1.26 g C m−2 d−1). The functional phenology was also captured by the MOD17A2H product (R = 0.62, RMSE = 9 days) in the Arctic. The spatial variation of the seasonal magnitude of GPP and its interannual trends is partly related to land cover types, peaking in forests and lowest in grasslands. The interannual trend of GPP decreased as the latitude and elevation increased, except for the latitude between 62°~66° N and elevation below 700 m. Our study not only revealed the variation of GPP in the Arctic but also helped to understand the carbon cycle over this region.

Is the grass always greener? Land surface phenology reveals differences in peak and season-long vegetation productivity responses to climate and management.

Vegetation phenology—the seasonal timing and duration of vegetative phases—is controlled by spatiotemporally variable contributions of climatic and environmental factors plus additional potential influence from human management. We used land surface phenology derived from the Advanced Very High Resolution Radiometer and climate data to examine variability in vegetation productivity and phenological dates from 1989 to 2014 in the U.S. Northwestern Plains, a region with notable spatial heterogeneity in climate, vegetation, and land use. We first analyzed interannual trends in six phenological measures as a baseline. We then demonstrated how including annual‐resolution predictors can provide more nuanced insights into measures of phenology between plant communities and across the ecoregion. Across the study area, higher annual precipitation increased both peak and season‐long productivity. In contrast, higher mean annual temperatures tended to increase peak productivity but for the majority of the study area decreased season‐long productivity. Annual precipitation and temperature had strong explanatory power for productivity‐related phenology measures but predicted date‐based measures poorly. We found that relationships between climate and phenology varied across the region and among plant communities and that factors such as recovery from disturbance and anthropogenic management also contributed in certain regions. In sum, phenological measures did not respond ubiquitously nor covary in their responses. Nonclimatic dynamics can decouple phenology from climate; therefore, analyses including only interannual trends should not assume climate alone drives patterns. For example, models of areas exhibiting greening or browning should account for climate, anthropogenic influence, and natural disturbances. Investigating multiple aspects of phenology to describe growing‐season dynamics provides a richer understanding of spatiotemporal patterns that can be used for predicting ecosystem responses to future climates and land‐use change. Such understanding allows for clearer interpretation of results for conservation, wildlife, and land management.

Assessment of smallholder farmers’ perception and adaptation response to climate change in the Olifants catchment, South Africa

Abstract Climate change is expected to affect the livelihood of rural farmers in South Africa, particularly the smallholder farmers, due to their overwhelming dependence on rain-fed agriculture. This study examines smallholder farmers' perception of climate change, the adaptation strategies adopted and factors that influence their adaptive decisions. The unit of data collection was household interview and focus group discussion. Climate data for the Olifants catchment (1986–2015) were also collected to validate farmers' perception of climate change with actual climate trend. Data collected were analysed using descriptive statistics, Mann–Kendall trend, Sen's slope estimator and multinomial logit regression model. Results revealed that smallholder farmers are aware of climate change (98%), their perception of these changes aligns with actual meteorological data, as the Mann–Kendall test confirms a decreasing inter-annual rainfall trend (−0.172) and an increasing temperature trend (0.004). These changes in temperature and precipitation have prompted the adoption of various adaptation responses, among which the use of improved seeds, application of chemical fertilizer and changing planting dates were the most commonly practised. The main barriers to the adoption of adaptation strategies were lack of access to credit facility, market, irrigation, information about climate change and lack of extension service. The implication of this study is to provide information to policy-makers on the current adaptation responses adopted by farmers and ways in which their adaptive capacity can be improved in order to ensure food security.

The role of teleconnection patterns in the variability and trends of growing season indices across Europe

AbstractTeleconnection patterns affect the weather and climate on both interannual and decadal timescales which in turn affects various socio‐economic sectors such as agriculture. We use three climate indices based on E‐OBS data from the INDECIS dataset (growing season onset [ogs10], growing season rainfall [gsr] and growing season temperature [ta_o]) to assess the interannual variability and trends over 1950–2017 associated with four teleconnection patterns (North Atlantic Oscillation [NAO], East Atlantic pattern [EA], Scandinavian pattern [SCA] and East Atlantic/West Russia pattern [EAWR]) using linear regression to extract the signal of each teleconnection pattern and their contribution to interannual variability. Trends towards an earlier growing season onset are found across most of Europe in low‐lying regions. The NAO dominates interannual variability in northwest Europe where an NAO index of 1 is associated with earlier ogs10 of about 10 days and the EA dominates the continent with a trend towards the positive EA phase driving an earlier growing season onset of 1.1–1.7 days·decade−1 in five regions (Great Britain and Ireland, France, Italy, Poland and North Germany, Hungary, Balkans). The EA and SCA gsr signals have north/south splits of orientation: positive EA is linked to increased gsr in northern regions and reduced gsr in southern Europe, and vice versa for SCA. Correlations between gsr interannual variability and the teleconnection contributions are strongest in the Mediterranean regions and south Scandinavia with maxima of 0.41 and 0.46, respectively. Decreasing ta_o trends in Romania are explained by poor data coverage causing problems with the EOBS gridding algorithm when new stations are incorporated from 1961. The net effect is that Romanian ta_o is about 1.5°C cooler than expected compared to trends from surrounding countries. Improved spatial and temporal data coverage will benefit the EOBS dataset and prevent such erroneous trends.

Tracking the dynamics of paddy rice cultivation practice through MODIS time series and PhenoRice algorithm

Monitoring paddy rice cultivation is essential for ensuring food security and for land resource management in agrarian countries of South Asia. In this study, we investigated the spatial and temporal variation of rice cultivated area and phenological metrics in Nepal between 2003 and 2018 using the time series MODIS data and PhenoRice algorithm. Comparisons of PhenoRice outputs with ancillary data show that implementation of PhenoRice with the MODIS data can be used for long-term change analysis of rice cultivation. Results on spatial distribution illustrate: rice cultivation is concentrated in the low elevation belt in the south; the cultivation begins earlier in the western region compared to the eastern region and begins earlier in the hilly region compared to the plains. The inter-annual trend analysis found a statistically significant decrease of rice cultivated area at the rate of 19.13 thousand hectares per year during the recent decade; the loss of rice fields was more prominent in the eastern plains while rice farming expanded in the mid-hills in the western region. Our study provides insights regarding timely and cost-efficient monitoring of rice farming at a large scale in the mountainous region.

Climate Change and Its Impacts on Farmer's Livelihood in Different Physiographic Regions of the Trans-Boundary Koshi River Basin, Central Himalayas.

The impact of climate change on farmers’ livelihoods has been observed in various forms at the local and regional scales. It is well known that the Himalayas region is affected by climate change, as reflected in the basic knowledge of farmers in the region. A questionnaire-based survey involving a total of 747 households was conducted to gather information on climate change and its impact, where the survey addressed four physiographic regions of the trans-boundary Koshi River Basin (KRB). Moreover, climatic data were used to calculate climatic trends between 1980 and 2018. The Mann–Kendall trend test was performed and the Sen’s slope calculated to analyze the inter-annual climatic trends over time. The survey noted that, for the basin, there was an increase in temperature, climate-induced diseases of crops, an increase in the frequency of pests as well as drought and floods and a decrease in rainfall, all which are strong indicators of climate change. It was perceived that these indicators had adverse impacts on crop production (89.4%), human health (82.5%), livestock (68.7%) and vegetation (52.1%). The observed climatic trends for all the physiographic regions included an increasing temperature trend and a decreasing rainfall trend. The rate of change varied according to each region, hence strongly supporting the farmers’ local knowledge of climate change. The highest increasing trend of temperature noted in the hill region at 0.0975 °C/a (p = 0.0002) and sharpest decreasing trend of rainfall in the mountain region by −10.424 mm/a (p = 0.016) between 1980 and 2018. Formulation of suitable adaptation strategies according to physiographic region can minimize the impact of climate change. New adaptation strategies proposed include the introduction of infrastructure for irrigation systems, the development of crop seeds that are more tolerant to drought, pests and disease tolerance, and the construction of local hospitals for the benefit of farming communities.

Linking long-term changes of the zooplankton community to the environmental variability at the EPEA Station (Southwestern Atlantic Ocean)

A significant sea surface temperature increase has been reported for the Southwestern Atlantic Ocean between 20° S-50° S over the last decades. Zooplankton organisms are highly sensitive to temperature rise. They play a very important role in marine ecosystems by providing the main pathway of energy transfer from primary producers to consumers. Seasonal and interannual (2000-2017) variability of metazooplankton in relation to environmental changes, particularly temperature, were analyzed at the EPEA station (38° 28′ S-57° 41′ W). Copepods, appendicularians, cladocerans, chaetognaths, and lamellibranch larvae were identified and quantified. Temperature exhibited a positive interannual trend during the series, whereas the Simpson parameter showed a decreasing tendency and salinity remained almost constant. Adults, copepodites, and nauplii of small copepods belonging to Oithonidae (mostly Oithona nana) and Paracalanidae-Clausocalanidae families dominated the metazooplankton community during the study period. Three groups of taxa with different seasonal patterns of variability were clearly identified. Members of Oithonidae exhibited positive interannual trends, whereas lamellibranch larvae and Calanidae showed negative interannual trends. A direct influence of temperature anomaly on these changes is suggested as well as possible indirect effects of this anomaly upon zooplankton through different phytoplankton fractions. Under the current scenario of climate change, the maintenance of this time-series becomes crucial in order to evaluate the eventual transfer of the environmental variability to the local food webs through planktonic organisms.

A MODIS-based scalable remote sensing method to estimate sowing and harvest dates of soybean crops in Mato Grosso, Brazil

Large-scale agriculture in the state of Mato Grosso, Brazil is a major contributor to global food supplies, but its continued productivity is vulnerable to contracting wet seasons and increased exposure to extreme temperatures. Sowing dates serve as an effective adaptation strategy to these climate perturbations. By controlling the weather experienced by crops and influencing the number of successive crops that can be grown in a year, sowing dates can impact both individual crop yields and cropping intensities. Unfortunately, the spatiotemporally resolved crop phenology data necessary to understand sowing dates and their relationship to crop yield are only available over limited years and regions. To fill this data gap, we produce a 500 m rainfed soy (Glycine max) sowing and harvest date dataset for Mato Grosso from 2004 to 2014 using a novel time series analysis method for Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery, adapted for implementation in Google Earth Engine (GEE). Our estimates reveal that soy sowing and harvest dates varied widely (about 2 months) from field to field, confirming the need for spatially resolved crop timing information. An interannual trend toward earlier sowing dates occurred independently of variations in wet season onset, and may be attributed to an improvement in logistic or economic constraints that previously hampered early sowing. As anticipated, double cropped fields in which two crops are grown in succession are planted earlier than single cropped fields. This difference shrank, however, as sowing of single cropped fields occurred closer to the wet season onset in more recent years. The analysis offers insights about sowing behavior in response to historical climate variations which could be extended to understand sowing response under climate change in Mato Grosso.

Spatial and Temporal Variation Characteristics of Snowfall in the Haihe River Basin from 1960 to 2016

The spatio-temporal variation of precipitation under global warming had been a research hotspot. Snowfall is an important part of precipitation, and its variabilities and trends in different regions have received great attention. In this paper, the Haihe River Basin is used as a case, and we employ the K-means clustering method to divide the basin into four sub-regions. The double temperature threshold method in the form of the exponential equation is used in this study to identify precipitation phase states, based on daily temperature, snowfall, and precipitation data from 43 meteorological stations in and around the Haihe River Basin from 1960 to 1979. Then, daily snowfall data from 1960 to 2016 are established, and the spatial and temporal variation of snowfall in the Haihe River Basin are analyzed according to the snowfall levels as determined by the national meteorological department. The results evalueted in four different zones show that (1) the snowfall at each meteorological station can be effectively estimated at an annual scale through the exponential equation, for which the correlation coefficient of each division is above 0.95, and the relative error is within 5%. (2) Except for the average snowfall and light snowfall, the snowfall and snowfall days of moderate snow, heavy snow, and snowstorm in each division are in the order of Zones III &gt; IV &gt; I &gt; II. (3) The snowfall and the number of snowfall days at different levels both show a decreasing trend, except for the increasing trend of snowfall in Zone I. (4) The interannual variation trend in the snowfall at the different levels are not obvious, except for Zone III, which shows a significant decreasing trend.

Bone Marrow Examination of Malignant Lymphomas in Korea: Eleven-Year Experience in a Single Institution

IntroductionMalignant lymphoma is a various group of disease with distinct histopathologic characteristic, innumerable clinical presentation and biologic behaviors. According to geographic location and races, malignant lymphoma shows diverse clinicopathologic characteristics. Initial diagnosis of lymphoma usually performed by pathologic review of lymph node or other tissue biopsies, however histopathologic examination, except bone marrow (BM), are sometimes not available because of patient's condition or clinical aspects. BM examination plays a role of the initial investigation in such cases. In this study, we analyzed the ratio of lymphoma BM involvement in total BM examination and ratio of BM as initial investigative specimen in lymphoma BM involvement during 11 years. Favored BM involvement pattern of each malignant lymphoma was also reviewed.MethodA total of 1164 cases were retrospectively reviewed for evaluation of BM lymphoma involvement from January 2006 to December 2016 at Gachon University Gil Medical Center in Korea. Two expert hematologists examined the lymphoma BM involvement and involvement pattern. BM involvement patterns were classified into five groups according to previous studies; focal nonparatrabecular, focal paratrabecular, intrasinosoidal, diffuse interstitial, and diffuse solid. Histopathologic diagnoses of lymph node or other tissues were reviewed if they were available. Statistical analysis was performed using spearman correlation for inter-annual trend assessment.ResultsA total of 6324 cases of BM studies were done during 11 years, of which 1164 (18.4%) were malignant lymphoma. 258 (22.2%) cases were initially investigated by BM specimen. The rate of initial investigation by BM specimen was eventually increased during study period (P=0.019); 17.3% in 2006 and 24.4% in 2016. Total numbers of total BM study tend to increase throughout study period (P< 0.001), however there was no statistically significant for annual increasing in proportion of malignant lymphoma (P=0.385).(Table 1)Of 1164 malignant lymphoma cases, 891 (76.7%) were B-cell lymphoma (BCL), 204 (17.6%) T/NK-cell lymphoma (TCL) and 67 (5.8%) Hodgkin lymphoma. Diffuse large B cell lymphoma (DLBCL) was most common in BCL(558, 48.0%), followed by marginal zone B-cell lymphoma of MALT(168, 14.5%), mantle cell lymphoma (MCL) (41, 3.5%), follicular lymphoma (FL) (36, 3.1%), Burkitt's lymphoma (BL) (20, 1.7%), and chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) (19, 1.6%). Among the TCLs, the commonest type was Extranodal NK/T-cell lymphoma (62, 5.3%), followed by Peripheral T-cell lymphoma (PTCL) (57, 4.9%), Angioimmunoblastic T-cell lymphoma (AITL) (43, 3.7%), Anaplastic large-cell lymphoma (19, 1.6%), and Enteropathy-associated T-cell lymphoma (10, 1.0%). Prevalence in DLBCL, FL and CLL/SLL was significantly increased during the study period (P<0.028).Of 202 cases of BM lymphoma involvement, 150(16.9%) were BCL, 45 (21.6%) TCL and 7 (9.9%) Hodgkin lymphoma. In BCL the most frequenly involved subtype was CLL/SLL (84.2%), followed by BL (70%), MCL (48.8%), FL (47.2%), and DLBCL (14.7%). In TCL, the most frequently involved subtype was PTCL (35.1%), followed by AITL (27.9%). (Table 2.)The most common involvement pattern of CLL was diffuse interstitial and diffuse solid (40%) pattern. BL showed diffuse solid pattern (55.6%) most commonly. MCL mainly presented both focal nonparatrabecular and diffuse interstitial(27.8%) patterns. FL showed focal paratrabecular and diffuse solid(31.3%) pattern. Diffuse interstitial pattern(39.7%) was mainly observed in DLBCL. PTCL showed diffuse interstitial(27.8%) pattern, AITL presented focal nonparatrabecular and diffuse interstitial pattern(33.3%). (Table 3.)ConslusionThis study demonstrates that initial investigation of malignant lymphoma by BM specimen increases during 2006-2016. The result may represent of growing importance of BM study as initial investigation tools for malignant lymphoma. Thus more efforts should be also made to diagnose malignant lymphoma by additional laboratory testing such as flowcytometry, chromosomal study, molecular study, and FISH analysis. Lymphoid neoplasm incidence and BM involvement frequency in western countries were well known through large-scale studies. This study also presented increasing in incidence of DLBCL, FL and CLL/SLL in Korean during 11 years [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series

Multi-temporal consistency of space-borne observations is an essential requirement for studying inter-annual changes and trends of satellite-derived biophysical products. The Proba-V mission, launched in 2013, was designed to ensure the continuity of the SPOT-VEGETATION long-term data record of global daily observations for land applications. The suitability of Proba-V to provide a temporally consistent data record is, however, potentially jeopardized by the orbital drift effect, which is known to induce spurious trends in time series. The aim of this paper is therefore to evaluate, for the first time, the orbital drift effect on Proba-V surface reflectance time series at 1 km resolution. In order to reliably identify such an effect, a two-fold approach is adopted. A simulation study is first defined to predict the temporal anomalies induced by the drifting illumination conditions. The numerical simulations are used as a benchmark to predict the impact of the drift for a range of sun-viewing angles. Real observations are then analyzed over a large set of land sites, globally spread and spanning a wide range of surface and environmental conditions. The surface anisotropy is characterized using the Ross-Thick Li-Sparse Reciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model. Both the simulation and the analysis of real observations consistently show that the orbital drift induces distinct and opposite trends in the two sides of the sensor across-track swath. Particularly, a positive drift is estimated in backward and a negative one in the forward scattering direction. When observations from all angular conditions are retained, these opposite trends largely compensate, with no remaining statistically significant drifts in time series of surface reflectances or Normalized Difference Vegetation Index (NDVI). As such, the Proba-V archive at 1 km resolution can be reliably used for inter-annual vegetation studies.

Multidimensional Verification of Anthropogenic VOCs Emissions Inventory Through Satellite Retrievals and Ground Observations

In this paper, a regional emissions inventory of anthropogenic VOCs was established based on the traditional emissions factor method for the Beijing-Tianjin-Hebei (BTH) region, followed by a multidimensional calibration study based on regional satellite remote sensing information for formaldehyde and typical urban ground VOCs. Inventory calculations showed that the VOCs emissions in BTH in 2013, 2015, and 2017 were 2026700, 2073400, and 1934200 tons, respectively, comprising alkanes (29.83% to 30.72%), unsaturated hydrocarbons (16.54% to 17.68%), aromatic hydrocarbons (27.14% to 27.51%), aldehydes (8.75% to 9.52%), ketones (8.13% to 9.04%), and aldehydes and ketones lipids (5.13% to 6.60%). During 2013-2017, the emission of VOCs in Zhangjiakou, Qinhuangdao, and Hengshui increased slightly (1.10% to 1.66% per year); emissions in Xingtai and Handan decreased slightly (-1.46% to -1.12% per year); and emissions in Chengde, Tangshan, Baoding, and Cangzhou were stable. There trends were consistent with the inter-annual trend of satellite-derived HCHO column concentrations. However, in Beijing, Tianjin, Langfang, and Shijiazhuang, annual variations in VOCs emissions (-6.51%, -3.30%, 2.16%, and 0.11% per year) and HCHO column concentrations (-1.17%, 7.19%, -0.24%, and 6.68% per year) were observed, respectively. In the regional VOCs inventory, a good linear correlation (R>0.5) was achieved between the grid emissions of VOCs and HCHO column concentrations in urban areas, while the correlation was only 0.33 in suburban areas. This is mainly due to the important influence of secondary conversion of VOCs originating from natural sources to HCHO in suburban areas. In addition, ground-level VOCs concentrations were observed in the urban areas of Beijing and Handan, where the emission ratios (ERs) of VOCs and CO were regressed. The ERs of most hydrocarbons were in good agreement with the regressed ERs, but the ERs of ethane were significantly lower (-156% to -73%) and the ERs of aromatic hydrocarbons above C8 were relatively high (54% to 74%). In general, the regional anthropogenic VOCs emissions inventory established in this paper offers high accuracy and reliability.