Interannual Trends Research Articles

A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests

Abstract. Tree phenology is a major driver of forest–atmosphere mass and energy exchanges. Yet, tree phenology has rarely been monitored in a consistent way throughout the life of a flux-tower site. Here, we used seasonal time series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (greenness chromatic coordinate), broadband NDVI, LAI (leaf area index), fAPAR (fraction of absorbed photosynthetic active radiation), CC (canopy closure), fRvis (fraction of reflected radiation) and GPP (gross primary productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest using an asymmetric double sigmoid function (ADS) fitted to the time series. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured the interannual variability of spring phenology very well (R2>0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) of less than 4 d. For the CC and GCC methods, mid-amplitude (50 %) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25 % of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 d. The ADS method used to derive the phenological markers provides the most biased estimates for the GPP and GCC. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2=0.63 and 0.80 for NDVI and CC, respectively), with an MAD of less than 7 d. Our results show that proximal-sensing methods can be used to derive robust phenological metrics. They can be used to retrieve long-term phenological series at eddy covariance (EC) flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.

Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover

Abstract. Mean sea ice thickness is a sensitive indicator of Arctic climate change and is in long-term decline despite significant interannual variability. Current thickness estimations from satellite radar altimeters employ a snow climatology for converting range measurements to sea ice thickness, but this introduces unrealistically low interannual variability and trends. When the sea ice thickness in the period 2002–2018 is calculated using new snow data with more realistic variability and trends, we find mean sea ice thickness in four of the seven marginal seas to be declining between 60 %–100 % faster than when calculated with the conventional climatology. When analysed as an aggregate area, the mean sea ice thickness in the marginal seas is in statistically significant decline for 6 of 7 winter months. This is observed despite a 76 % increase in interannual variability between the methods in the same time period. On a seasonal timescale we find that snow data exert an increasingly strong control on thickness variability over the growth season, contributing 46 % in October but 70 % by April. Higher variability and faster decline in the sea ice thickness of the marginal seas has wide implications for our understanding of the polar climate system and our predictions for its change.

Sensitivity of spring phenology to elevation in Qinling Mountains, China

Vegetation phenology, a regular and periodic phenomenon in nature, is an important indicator for natural environment, especially climate change. The study of spatiotemporal variations of vegetation phenology is of great significance for monitoring the changes of terrestrial vegetation. In this study, the Savitzky-Golay (S-G) filtering method was used to reconstruct time-series MODIS enhanced vegetation index (EVI) data in the Qinling Mountains from 2001 to 2018. The dynamic threshold method was used to extract the spring phenological parameter (start of growth season, SOS). The correlation between multi-year mean SOS and interannual variation with altitude and slope was analyzed. The results showed that SOS was delayed by 1.82 d with every 100 m increase in altitude in the Qinling Mountains. The interannual change trends of SOS mainly concentrated in 0-5 d·(10 a)-1. The pixels with delaying trend were mainly distributed in low-altitude regions, with the delaying degree being gradually decreased with the elevation. The interannual change trend of SOS in high-altitude regions was more complex than that in lower-altitude regions. The multi-year average SOS in the northern slope was approximately 2.9 d earlier than that of the southern slope, whereas the southern slope had a more significant advancing trend. The interannual change trends of SOS in both north and south slopes showed a delaying trend in low-altitude, with little difference between north and south slopes. The advancing trend in middle and high altitude was significantly different.

A Process‐Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services

AbstractTerrestrial ecosystems provide multiple services interacting in complex ways. However, most ecosystem services (ESs) models (e.g., InVEST and ARIES) ignored the relationships among ESs. Process‐based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes. Therefore, based on the Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA) models, we developed a process‐based ES model (CEVSA‐ES) integrating remotely sensed leaf area index to evaluate four important ESs (i.e., productivity provision, carbon sequestration, water retention, and soil retention) at annual timescale in China. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA‐ES model was the consideration of soil erosion processes and its impact on carbon cycling. The new version also improved the carbon‐water cycle algorithms. Then, the Sobol and DEMC methods that integrated the CEVSA‐ES model with nine flux sites comprising 39 site‐years were used to identify and optimize parameters. Finally, the model using the optimized parameters was validated at 26 field sites comprising 135 site‐years. Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% interannual variabilities of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. The CEVSA‐ES model performed well for productivity provision and carbon sequestration, which explained 96% and 81% of the spatial‐temporal variations of the observed annual productivity provision and carbon sequestration, respectively. The model also captured the interannual trends of water retention and soil erosion for most sites or basins.

Multiannual trends (2004–2019) in the abundance of larvae of the invasive mussel Limnoperna fortunei and crustacean zooplankton in a large South American reservoir

AbstractThe behavior of non‐indigenous species with time since introduction varies widely and is a major issue in the understanding of their long‐term effects on the habitats colonised. We analysed the long‐term trends in the densities of larvae of the invasive mussel Limnoperna fortunei and planktonic Copepoda and Cladocera in Salto Grande, a large South American reservoir in the Uruguay River (Argentina‐Uruguay), on the basis of weekly samples collected between June 2004 and November 2019. All three groups depicted variable interannual abundances. While mussel veligers showed no significant interannual trends (overall mean: 2552 larvae L−1), crustaceans decreased significantly in the time elapsed (Copepoda: from ˜1000 to 100 ind. L−1; Cladocera: from ˜500 to 100 ind. L−1). We conclude that increasing discharge rates by the Uruguay River and the concomitant decreases in reservoir water residence times are the most likely cause for these drops in their populations. As opposed to many other invasive species elsewhere, and to the same mussel in other South American waterbodies, L. fortunei larvae did not change in abundance with time since introduction. Our results underscore the assumption that each biological introduction is a particular and highly context‐dependent event and the dynamics of biological invasions in general elude broad generalizations.

The advective Brewer–Dobson circulation in the ERA5 reanalysis: climatology, variability, and trends

Abstract. The stratospheric Brewer–Dobson circulation (BDC) is an important element of climate as it determines the transport and distributions of key radiatively active atmospheric trace gases, which affect the Earth's radiation budget and surface climate. Here, we evaluate the interannual variability, climatology, and trends of the BDC in the ERA5 reanalysis and intercompare them with its predecessor, the ERA-Interim reanalysis, for the 1979–2018 period. We also assess the modulation of the circulation by the Quasi-Biennial Oscillation (QBO) and the El Niño–Southern Oscillation (ENSO), as well as the forcings of the circulation by the planetary and gravity wave drag. The comparison of ERA5 and ERA-Interim reanalyses shows a very good agreement in the morphology of the BDC and in its structural modulations by the natural variability related to QBO and ENSO. Despite the good agreement in the spatial structure, there are substantial and significant differences in the strength of the BDC and natural variability impacts on the BDC between the two reanalyses, particularly in the upper troposphere and lower stratosphere (UTLS) and in the upper stratosphere. Throughout most regions of the stratosphere, the variability and trends of the advective BDC are stronger in the ERA5 reanalysis due to stronger planetary and gravity wave forcings, except in the UTLS below 20 km where the tropical upwelling is up to 40 % weaker mainly due to a significantly weaker gravity wave forcing at the equatorial-ward upper flank of the subtropical jet. In the extratropics, the large-scale downwelling is stronger in ERA5 than in ERA-Interim that is linked to significant differences in planetary and gravity wave forcings in the upper stratosphere. Analysis of the BDC trend shows a global insignificant acceleration of the annual mean residual circulation with an acceleration rate of about 1.5 % decade−1 at 70 hPa due to the long-term intensification in gravity and planetary wave breaking, consistent with observed and modelled BDC changes. Our findings suggest that the advective BDC from the kinematic ERA5 reanalysis is well suited for climate model validation in the UTLS and mid-stratosphere when using the standard formula of zonally averaged zonal momentum equation. The reported differences between the two reanalyses may also affect the nudged climate model simulations. Therefore, additional studies are needed to investigate whether or not nudging climate models toward ERA5 reanalysis will reproduce the upwelling trends from free-running simulations and from ERA5. Finally, further studies are also needed to better understand the impact of the new non-orographic gravity wave parameterization scheme, higher model top, and the representation of the sponge layer in ERA5 on the differences in the upper stratosphere and polar regions.

Temporal evolution of mid-tropospheric CO2 over the Indian Ocean

The rapid increase in carbon dioxide (CO2) in the atmosphere due to anthropogenic activities aids global warming that demands immediate attention and policy actions. The changes in weather patterns, such as winds, temperature and precipitation, leading to floods and droughts are linked to the increasing greenhouse gases including CO₂. This situation warrants close and continuous monitoring of atmospheric CO2. Here, the mid-tropospheric CO2 from Atmospheric Infrared Sounder (AIRS) is analysed for the past 14 years over Indian Ocean. The northern hemisphere has a higher CO2 concentration than the southern hemisphere, as most of the landmasses with significant CO2 sources are present in the former region. Seasonal variations show the highest CO2 concentration over the north Indian Ocean (NIO) during MAM (March, April and May), about 388.55 ppm, and lowest during ON (October and November), about 386.92 ppm. In contrast, the south Indian Ocean shows the highest, but the magnitude is smaller than that over NIO, during December, January and February. Arabian Sea (AS) exhibits smaller CO2 concentrations compared to that over Bay of Bengal (BoB). However, AS shows the highest trend of 2.13 ppm/year during the post-monsoon season (ON). The seasonal correlation analysis of the temperature at 10 m and sea surface temperature against mid-tropospheric CO2 shows a higher positive correlation for BoB. These analyses, therefore, provide a detailed understanding of the basin-wide seasonal and inter-annual trends of mid-tropospheric CO2 over Indian Ocean.

The Bowen Ratio of an Alpine Grassland in Three-River Headwaters, Qinghai-Tibet Plateau, from 2001 to 2018

The Bowen ratio (β) is used to quantify heat transfer from the land surface into the air, which is becoming a hot topic in research on the biogeophysical effects of land use and cover changes. The Three-River Headwaters (TRH), as a sensitive and fragile region, was selected as the study area. The β for 2001–2018 was estimated from the evapotranspiration product (ETMOD16) of MODIS and the net radiation of the land surface through the albedo from GLASS. The ETMOD16 data were evaluated against the observation data (ETOBS) at two alpine grassland flux towers obtained from ChinaFLUX. The interannual trend of the β was analyzed by multiple linear regression (MLR) and structure model (SEM) with the multiple factors of precipitation, temperature, humidity, albedo, and normalized difference vegetation index (NDVI, MOD09Q1). The results show that the ETMOD16 values were significantly correlated with ETOBS, with a correlation coefficient above 0.70 (P < 0.01) for the two sites. In 2001–2018, the regional mean β was 2.52 ± 0.77 for the whole grassland, and its spatial distribution gradually increased from the eastern to western region. The interannual β showed a downward trend with a slope of –0.025 and a multiple regression coefficient (R2) of 0.21 (P = 0.056). Most of the variability (51%) in the interannual β can be explained by the linear regression of the above multiple factors, and the temperature plays a dominant role for the whole region. The SEM analysis further shows that an increasing NDVI results in a decreasing albedo with a path coefficient of –0.57, because the albedo was negatively correlated with NDVI (R2 = 0.52, P < 0.01), which indicates a negative and indirect effect on β from vegetation restoration. An obvious warming climate was found to prompt more evapotranspiration, and restoring vegetation makes the land surface receive more radiation, which both resulted in a decreasing trend in the annual β. This study revealed the biogeophysical mechanisms of vegetation restoration under a changing climate, and demonstrated the Bowen ratio can be applied as an indicator of climate-regulating functions in ecosystem assessments.

The Relationship Between the Zonal Index and Annular Mode Index in Reanalysis and CMIP5 Models

The annular mode at the surface, referred to as the Arctic Oscillation (AO) in the Northern Hemisphere and the Antarctic Oscillation (AAO) in the Southern Hemisphere, is statistically defined by the leading mode of variability of sea level pressure (SLP) or geopotential height. Its principal component time series is set as the AO or AAO index. Although this metric is widely used, it needs an empirical orthogonal function analysis which introduces the complexity in the multi-model analysis. As an alternative measure, the zonal index (ZI), which is traditionally defined as the zonal-mean SLP difference between the two reference latitudes, has also been used. Here we re-evaluate the interannual and trend relationships between the ZI and the AO/AAO index using the two reanalysis datasets and 35 climate model simulations for both the present and future climate. For all datasets, the spatio-temporal variability of the Southern-Hemisphere ZI is almost identical to that of the AAO index. The ZI is still useful to examine the AO-related circulation variability and change in the Northern Hemisphere but mostly in the cold season. The data-dependent ZI, optimized for each data, exhibits a rather weak relationship with the AO index. This result suggests that the interannual variability and long-term trend of the midlatitude circulation can be concisely quantified by computing the fixed-latitude ZI in all seasons in the Southern Hemisphere and in the cold season in the Northern Hemisphere, not only for reanalysis data but also for climate model datasets which have significant biases varying from model to model.

IASI-Derived Sea Surface Temperature Data Set for Climate Studies.

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean).

Brazil Current Volume Transport Variability During 2009–2015 From a Long‐Term Moored Array at 34.5°S

AbstractThe Brazil Current, the western limb of the subtropical gyre of the South Atlantic Ocean, is one of the major Western Boundary Currents of the global ocean. Here, we present the first multiyear continuous daily time series of Brazil Current absolute volume transport obtained using 6+ years of observations from a line of four pressure‐recording inverted echo sounders (PIES) deployed at 34.5°S. The array was augmented in December 2012 with two current meter‐equipped PIES and in December 2013 with a moored Acoustic Doppler Current Profiler on the upper continental slope. The Brazil Current is bounded by the sea surface and the neutral density interface separating South Atlantic Central Water and Antarctic Intermediate Water, which is on average at a reference pressure of 628 ± 46 dbar, and it is confined west of 49.5°W. The Brazil Current has a mean strength of −14.0 ± 2.8 Sv (1 Sv ≡ 106 m3 s−1; negative indicates southward flow) with a temporal standard deviation of 8.8 Sv and peak‐to‐peak range from −41.7 to +20 Sv. About 80% of the absolute transport variance is concentrated at periods shorter than 150 days with a prominent peak at 100 days. The baroclinic component accounts for 85% of the absolute transport variance, but the barotropic variance is not negligible. The baroclinic and barotropic transports are uncorrelated, demonstrating the need to measure both transport components independently. Given the energetic high frequency transport variations, statistically significant seasonal to interannual variability and trends have yet to be detected.

Similarities and disparities in urban local heat islands responsive to regular-, stable-, and counter-urbanization: A case study of Guangzhou, China

Understanding the dynamics and spatial heterogeneity of the intra-city surface heat island (herein termed the surface urban heat island, SUHI) is critical for the design of urban heat mitigation strategies. Large disparities in the spatiotemporal variations of SUHIs are anticipated under different urbanization processes. However, most previous studies have focused solely on the inter-annual spatiotemporal SUHI variations of regular urbanization, while those for stable- and counter-urbanization remain largely unknown. Based on the remote identification of these three urbanization types over Guangzhou, China, we propose a novel strategy to investigate simultaneously the spatiotemporal variations and the associated controls of SUHIs. Our results indicate that: (1) Counter-, regular-, and stable-urbanization occurs mainly over the city center, city periphery, and the in-between areas, respectively. (2) The three urbanization types all demonstrate similar and significant growth in the daytime local SUHI intensity (SUHII). (3) There are significant disparities in the contributions of controls to the inter-annual daytime SUHII trends for these three urbanization processes. For the regular urbanization, the increase of the impervious surface percentage (ISP) dominates daytime SUHII growth, while the combination of ISP and residual factors (e.g., background climate and 3D urban geometry) leads for counter urbanization. For stable urbanization, the combination of residual controls and the increase in population density is the major factor. Urban divisional management may contribute to the mitigation of intra-city SUHI. Our findings potentially advance our understanding of changes in urban thermal environment under different urbanization processes.

Consistency and Homogeneity of Atmospheric Energy, Moisture, and Mass Budgets in ERA5

AbstractThis study uses advanced numerical and diagnostic methods to evaluate the atmospheric energy budget with the fifth major global reanalysis produced by ECMWF (ERA5) in combination with observed and reconstructed top of the atmosphere (TOA) energy fluxes for the period 1985–2018. We assess the meridional as well as ocean–land energy transport and perform internal consistency checks using mass-balanced data. Furthermore, the moisture and mass budgets in ERA5 are examined and compared with previous budget evaluations using ERA-Interim as well as observation-based estimates. Results show that peak annual mean meridional atmospheric energy transports in ERA5 (4.58 ± 0.07 PW in the Northern Hemisphere) are weaker compared to ERA-Interim (4.74 ± 0.09 PW), where the higher spatial and temporal resolution of ERA5 can be excluded as a possible reason. The ocean–land energy transport in ERA5 is reliable at least from 2000 onward (~2.5 PW) such that the imbalance between net TOA fluxes and lateral energy fluxes over land are on the order of ~1 W m−2. Spinup and spindown effects as revealed from inconsistencies between analyses and forecasts are generally smaller and temporally less variable in ERA5 compared to ERA-Interim. Evaluation of the moisture budget shows that the ocean–land moisture transport and parameterized freshwater fluxes agree well in ERA5, while there are large inconsistencies in ERA-Interim. Overall, the quality of the budgets derived from ERA5 is demonstrably better than estimates from ERA-Interim. Still some particularly sensitive budget quantities (e.g., precipitation, evaporation, and ocean–land energy transport) show apparent inhomogeneities, especially in the late 1990s, which warrant further investigation and need to be considered in studies of interannual variability and trends.

Spatio-temporal changes of snowmelt in Greenland ice sheet based on SSM/I (SSMIS) data (1988-2016)

The snowmelt of the Greenland ice sheets is of great significance to the study of global climate change. This paper is based on the 19.35 GHz horizontal polarization data and 37.00 GHz vertical polarization data of the Special Sensor Microwave/ Imager Sounder (SSMIS) and Special Sensor Microwave/ Image (SSM/I) carried by National Defense Meteorological Satellite Program (DMSP) from 1988 to 2016, by cross-polarization ratio (XPGR) algorithm (threshold value is -0.0158). The inter-annual trends of snowmelt area, annual average snowmelt onset, end date and duration in Greenland were studied. The results showed that the maximum snowmelt area was 2,080,000 km2 in 2012, and the minimum was 1,115,000 km2 in 1992. From 1988 to 2016, the snowmelt area of the Greenland ice sheets was increased by 2.8×105 km2, with a growth rate of 9.66×103 km2/year. In the annual average change rate, there were earlier snowmelt onset date (0.16 days earlier each year), longer snowmelt duration (0.36 days longer each year) and later snowmelt end date (0.06 days later each year), and the snowmelt area was in the marginal region. The snowmelt area of the southern margin is the largest, and there are obvious regional differences. The snowmelt of Greenland ice sheets changes greatly and shows a periodic change rule in the annual mean snowmelt variation.

Fifteen-year Variations of Water Use Efficiency over a Wheat-Maize Rotation Cropland in the North China Plain

Based on fifteen-year observations, we explored the interannual variations of five water use efficiency (WUE) indicators (i.e., standard ecosystem WUE (EWUE), plant-canopy WUE (PWUE), inherent WUE (IWUE), underlying WUE (uWUE) and stomatal slope (G1)) over a typical winter wheat-summer maize rotation cropland in the North China Plain. The results showed that none of WUE indicators had a significant interannual trend. PWUE showed the lowest interannual variability and could be regarded as stationary for these two crops. G1 was stationary for wheat which was under an essentially constant soil water condition, whereas exhibited a strong fluctuation for maize experiencing more variable soil water content (SWC) than wheat. IWUE and uWUE were non-stationary for both crops. SWC and leaf area index (LAI) dominated EWUE for maize and wheat, respectively. PWUE was dominated by the shortwave solar radiation (Rg) in maize seasons and not significantly affected by any meteorological or biophysical factors in wheat seasons. For IWUE and uWUE, air temperature (Ta) and LAI were the most important drivers in maize and wheat seasons, respectively. Atmospheric CO2 concentration (Ca) and vapor pressure deficit (VPD) were not revealed as important controllers. These findings reflected that the meteorological and biophysical regulatory mechanisms of WUE indicators at the ecosystem level were quite different from that at the leaf level. But G1 was an exception due to that for both maize and wheat, G1 was only significantly affected by SWC. Moreover, we found that none of the remote sensing (RS)-based global products performed satisfactorily on capturing interannual variations of EWUE for both crops. These results cautioned that using these global RS-based products to explore the interannual variations of EWUE for the cropland ecosystem may lead to substantial bias.

A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation

BackgroundLong-term surface NO2 data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO2 observations for Mainland China before 2013, training a model with 2013–2018 data to make predictions for 2005–2012 (back-extrapolation) could cause substantial estimation bias due to concept drift. ObjectiveThis study aims to correct the estimation bias in order to reconstruct the spatiotemporal distribution of daily surface NO2 levels across China during 2005–2018. MethodsOn the basis of ground- and satellite-based data, we proposed the robust back-extrapolation with a random forest (RBE-RF) to simulate the surface NO2 through intermediate modeling of the scaling factors. For comparison purposes, we also employed a random forest (Base-RF), as a representative of the commonly used approach, to directly model the surface NO2 levels. ResultsThe validation against Taiwan’s NO2 observations during 2005–2012 showed that RBE-RF adequately corrected the substantial underestimation by Base-RF. The RMSE decreased from 10.1 to 8.2 µg/m3, 7.1 to 4.3 µg/m3, and 6.1 to 2.9 µg/m3 in predicting daily, monthly, and annual levels, respectively. For North China with the most severe pollution, the population-weighted NO2 ([NO2]pw) during 2005–2012 was estimated as 40.2 and 50.9 µg/m3 by Base-RF and RBE-RF, respectively, i.e., 21.0% difference. While both models predicted that the national annual [NO2]pw increased during 2005–2011 and then decreased, the interannual trends were underestimated by >50.2% by Base-RF relative to RBE-RF. During 2005–2018, the nationwide population that lived in the areas with NO2 > 40 µg/m3 were estimated as 259 and 460 million by Base-RF and RBE-RF, respectively. ConclusionWith RBE-RF, we corrected the estimation bias in back-extrapolation and obtained a full-coverage dataset of daily surface NO2 across China during 2005–2018, which is valuable for environmental management and epidemiological research.

Declines and recovery in endangered Galapagos pinnipeds during the El Ni\xf1o event

Currently, the Galapagos sea lion (GSL, Zalophus wollebaeki) and Galapagos fur seal (GFS, Arctocephalus galapagoensis) are among the most important endemic species for conservation in the Galapagos Archipelago. Both are classified as “Endangered” since their populations have undergone drastic declines over the last several decades. In this study we estimated the abundance of both otariids, and their population trends based using counts conducted between 2014 and 2018 in all their rookeries, and we analyzed the influence of environmental variability on pup production. The GSL population size in 2018 in the archipelago was estimated to be between 17,000 to 24,000 individuals and has increased at an average annual rate of 1% over the last five years after applying correction factors. The highest number of GSL counted in the archipelago was in 2014 followed by a population decline of 23.8% in 2015 that was associated with the El Niño event that occurred during that year. Following this event, the population increased mainly in the northern, central and southeastern rookeries. The GSL pup abundance showed a decreasing trend with the increase in intensity of the El Niño. The GFS population in 2018 was counted in 3,093 individuals and has increased at an annual rate of 3% from 2014 to 2018. A high number of GFS counted in 2014 was followed by a population decrease of 38% in 2015, mainly in the western rookeries. There was interannual population fluctuations and different growth trends among regions of the archipelago. GSL and GFS pup abundance has a strong decreasing tendency with the increase in the subthermocline temperature (ST) and the El Niño 1 + 2 index. Our results provide evidence that both species are highly vulnerable to periodic oceanographic-atmospheric events in the Galapagos Archipelago which impact prey abundance and the flow of energy in the unique Galapagos ecosystem.

Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products

The science teams of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) have been supporting various global climate, biogeochemistry, and energy flux research efforts by producing valuable long-term Leaf Area Index (LAI) products. Although intensive LAI validation studies have been carried out globally, retrieval accuracy has been assessed only over short time spans and product quality is reported as a constant based on the assumption that it is stationary. However, a preliminary evaluation found a time-dependent signal degradation of the MODIS sensors which may cause the uncertainty of the Bidirectional Reflectance Factor (BRF) product to exceed the preset uncertainty buffer in the retrieval algorithm and result in MODIS LAI quality variation and time-series inconsistency with VIIRS LAI. Therefore, to ensure the reliability of trend-detection studies and to answer the critical question of whether the algorithm configuration is still suitable for the uncertainty level of present BRF product, there is a need for a more comprehensive investigation regarding the performance stability of LAI retrieval algorithm due to model uncertainty and input uncertainties. This paper reports analyses of inter-annual stability and trends of LAI uncertainties (inferred from product quality flag rather than ground-based validation) as well as LAI magnitudes using multi-year (MODIS: 2001–2019, VIIRS: 2013–2019) and multi-site (445 sites) datasets. We found that the quality metrics of the two products are consistent across different biome types and LAI values (R2 ranged from 0.88 to 0.99). In the 19-year MODIS period, we found a significant increasing trend in LAI magnitudes (4.5%/decade) which agrees with previous reports, while there was no significant trend in product quality. Moreover, quality metrics showed much smaller inter-annual variations than LAI values, which confirms the performance stability of retrieval algorithms and suggests that the configuration of MODIS algorithm is still compatible with the uncertainty of algorithm inputs. Additionally, this study provides support to the “Greening Asia” studies by eliminating the possibility that the “greening” could be attributed to artificial trends caused by sensor degradation. We also found very small inter-annual variations in atmospheric conditions (e.g., cloud and aerosols), which further assures that the performance stability of retrieval algorithm. Overall, our results demonstrate the robustness of the retrieval algorithm in the presence of changes in input uncertainties. The temporal stability of the algorithm performance indirectly strengthen the confidence in the continuous use of MODIS LAI products either independently or in combination with its successor - VIIRS LAI - to study global and regional vegetation dynamics

Standardized flux seasonality metrics: a companion dataset for FLUXNET annual product

Abstract. Phenological events are integrative and sensitive indicators of ecosystem processes that respond to climate, water and nutrient availability, disturbance, and environmental change. The seasonality of ecosystem processes, including biogeochemical fluxes, can similarly be decomposed to identify key transition points and phase durations, which can be determined with high accuracy, and are specific to the processes of interest. As the seasonality of different processes differ, it can be argued that the interannual trends and responses to environmental forcings can be better described through the fluxes' own temporal characteristics than through correlation to traditional phenological events like bud break or leaf coloration. Here we present a global dataset of seasonality or phenological metrics calculated for gross primary productivity (GPP), ecosystem respiration (RE), latent heat (LE), and sensible heat (H) calculated for the FLUXNET2015 Dataset of about 200 sites and 1500 site years of data. The database includes metrics (i) on an absolute flux scale for comparisons with flux magnitudes and (ii) on a normalized scale for comparisons of change rates across different fluxes. Flux seasonality was characterized by fitting a single-pass double-logistic model to daily flux integrals, and the derivatives of the fitted time series were used to extract the phenological metrics marking key turning points, season lengths, and rates of change. Seasonal transition points could be determined with a 90 % confidence interval of 6–11 d for GPP, 8–14 d for RE, 10–15 d for LE, and 15–23 d for H. The phenology metrics derived from different partitioning methods diverged, at times significantly. This Flux Seasonality Metrics Database (FSMD) can be accessed at the US Department of Energy's (DOE) Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE, https://doi.org/10.15485/1602532; Yang and Noormets, 2020). We hope that it will facilitate new lines of research, including (1) validating and benchmarking ecosystem process models, (2) parameterizing satellite remote sensing phenology and PhenoCam products, (3) optimizing phenological models, and (4) generally expanding the toolset for interpreting ecosystems responses to changing climate.

Interannual Variability and Trends in Sea Surface Temperature, Lower and Middle Atmosphere Temperature at Different Latitudes for 1980–2019

The influence of sea-surface temperature (SST) on the lower troposphere and lower stratosphere temperature in the tropical, middle, and polar latitudes is studied for 1980–2019 based on the MERRA2, ERA5, and Met Office reanalysis data, and numerical modeling with a chemistry-climate model (CCM) of the lower and middle atmosphere. The variability of SST is analyzed according to Met Office and ERA5 data, while the variability of atmospheric temperature is investigated according to MERRA2 and ERA5 data. Analysis of sea surface temperature trends based on reanalysis data revealed that a significant positive SST trend of about 0.1 degrees per decade is observed over the globe. In the middle latitudes of the Northern Hemisphere, the trend (about 0.2 degrees per decade) is 2 times higher than the global average, and 5 times higher than in the Southern Hemisphere (about 0.04 degrees per decade). At polar latitudes, opposite SST trends are observed in the Arctic (positive) and Antarctic (negative). The impact of the El Niño Southern Oscillation phenomenon on the temperature of the lower and middle atmosphere in the middle and polar latitudes of the Northern and Southern Hemispheres is discussed. To assess the relative influence of SST, CO2, and other greenhouse gases’ variability on the temperature of the lower troposphere and lower stratosphere, numerical calculations with a CCM were performed for several scenarios of accounting for the SST and carbon dioxide variability. The results of numerical experiments with a CCM demonstrated that the influence of SST prevails in the troposphere, while for the stratosphere, an increase in the CO2 content plays the most important role.