Deep Neural Network Cloud-Type Classification (DeepCTC) model and its application in evaluating PERSIANN-CCS

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation

Decadal variations in NDVI and food production in India

In this study we use long-term satellite, climate, and crop observations to document the spatial distribution of the recent stagnation in food grain production affecting the water-limited tropics (WLT), a region where 1.5 billion people live and depend on local agriculture that is constrained by chronic water shortages. Overall, our analysis shows that the recent stagnation in food production is corroborated by satellite data. The growth rate in annually integrated vegetation greenness, a measure of crop growth, has declined significantly (p < 0.10) in 23 of the WLT cropland area during the last decade, while statistically significant increases in the growth rates account for less than 2. Inmost countries, the decade-long declines appear to be primarily due to unsustainable crop management practices rather than climate alone. One quarter of the statistically significant declines are observed in India, which with the world's largest population of food-insecure people and largest WLT croplands, is a leading example of the observed declines. Here we show geographically matching patterns of enhanced crop production and irrigation expansion with groundwater that have leveled off in the past decade. We estimate that, in the absence of irrigation, the enhancement in dry-season food grain production in India, during 1982-2002, would have required an increase in annual rainfall of at least 30 over almost half of the cropland area. This suggests that the past expansion of use of irrigation has not been sustainable. We expect that improved surface and groundwater management practices will be required to reverse the recent food grain production declines. © 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland

Increasing trends of soil greenhouse gas fluxes in Japanese forests from 1980 to 2009

Forest soils are a source/sink of greenhouse gases, and have significant impacts on the budget of these terrestrial greenhouse gases. Here, we show climate-driven changes in soil GHG fluxes (CO2 emission, CH4 uptake, and N2O emission) in Japanese forests from 1980 to 2009, which were estimated using a regional soil GHG model that is data-oriented. Our study reveals that the soil GHG fluxes in Japanese forests have been increasing over the past 30 years at the rate of 0.31 Tg C yr−2 for CO2 (0.23 % yr−1, relative to the average from 1980 to 2009), 0.40 Gg C yr−2 for CH4 (0.44 % yr−1), and 0.0052 Gg N yr−2 for N2O (0.27 % yr−1). Our estimates also show large interannual variations in soil GHG fluxes. The increasing trends and large interannual variations in soil GHG fluxes seem to substantially affect Japan's Kyoto accounting and future GHG mitigation strategies

Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data

Background. Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. Methodology/Principal Findings. We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Conclusions/Significance. Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling

Impacts of large-scale climatic disturbances on the terrestrial carbon cycle

BACKGROUND: The amount of carbon dioxide in the atmosphere steadily increases as a consequence of anthropogenic emissions but with large interannual variability caused by the terrestrial biosphere. These variations in the CO(2 )growth rate are caused by large-scale climate anomalies but the relative contributions of vegetation growth and soil decomposition is uncertain. We use a biogeochemical model of the terrestrial biosphere to differentiate the effects of temperature and precipitation on net primary production (NPP) and heterotrophic respiration (Rh) during the two largest anomalies in atmospheric CO(2 )increase during the last 25 years. One of these, the smallest atmospheric year-to-year increase (largest land carbon uptake) in that period, was caused by global cooling in 1992/93 after the Pinatubo volcanic eruption. The other, the largest atmospheric increase on record (largest land carbon release), was caused by the strong El Niño event of 1997/98. RESULTS: We find that the LPJ model correctly simulates the magnitude of terrestrial modulation of atmospheric carbon anomalies for these two extreme disturbances. The response of soil respiration to changes in temperature and precipitation explains most of the modelled anomalous CO(2 )flux. CONCLUSION: Observed and modelled NEE anomalies are in good agreement, therefore we suggest that the temporal variability of heterotrophic respiration produced by our model is reasonably realistic. We therefore conclude that during the last 25 years the two largest disturbances of the global carbon cycle were strongly controlled by soil processes rather then the response of vegetation to these large-scale climatic events

Responses of grape berry anthocyanin and tritratable acidity to the projected climate change across the Western Australian wine regions

More than a century of observations has established that climate influences grape berry composition. Accordingly, the projected global climate change is expected to impact on grape berry composition although the magnitude and direction of impact at regional and subregional scales are not fully known. The aim of this study was to assess potential impacts of climate change on levels of berry anthocyanin and titratable acidity (TA) of the major grapevine varieties grown across all of the Western Australian (WA) wine regions. Grape berry anthocyanin and TA responses across all WA wine regions were projected for 2030, 2050 and 2070 by utilising empirical models that link these berry attributes and climate data downscaled (to ∼5 km resolution) from the csiro_mk3_5 and miroc3_2_medres global climate model outputs under IPCC SRES A2 emissions scenario. Due to the dependence of berry composition on maturity, climate impacts on anthocyanin and TA levels were assessed at a common maturity of 22 °Brix total soluble solids (TSS), which necessitated the determination of when this maturity will be reached for each variety, region and warming scenario, and future period.The results indicate that both anthocyanin and TA levels will be affected negatively by a warming climate, but the magnitude of the impacts will differ between varieties and wine regions. Compared to 1990 levels, median anthocyanins concentrations are projected to decrease, depending on global climate model, by up to 3–12 % and 9–33 % for the northern wine regions by 2030 and 2070, respectively while 2–18 % reductions are projected in the southern wine regions for the same time periods. Patterns of reductions in the median Shiraz berry anthocyanin concentrations are similar to that of Cabernet Sauvignon; however, the magnitude is lower (up to 9–18 % in southern and northern wine regions respectively by 2070). Similarly, uneven declines in TA levels are projected across the study regions. The largest reductions in median TA are likely to occur in the present day warmer wine regions, up to 40 % for Chardonnay followed by 15 % and 12 % for Shiraz and Cabernet Sauvignon, respectively, by 2070 under the high warming projection (csiro_mk3_5). It is concluded that, under existing management practices, some of the key grape attributes that are integral to premium wine production will be affected negatively by a warming climate, but the magnitudes of the impacts vary across the established wine regions, varieties, the magnitude of warming and future periods considered

Changes in global groundwater organic carbon driven by climate change and urbanization

YesClimate change and urbanization can increase pressures on groundwater resources, but little is known about how groundwater quality will change. Here, we rely on a global synthesis (n = 9,404) to reveal the drivers of dissolved organic carbon (DOC), which is an important component of water chemistry and substrate for microorganisms which control many biogeochemical reactions. Groundwater ions, local climate and land use explained ~ 31% of observed variability in groundwater DOC, whilst aquifer age explained an additional 16%. We identify a 19% increase in DOC associated with urban land cover. We predict major groundwater DOC increases following changes in precipitation and temperature in key areas relying on groundwater. Climate change and conversion of natural or agricultural areas to urban areas will decrease groundwater quality and increase water treatment costs, compounding existing threats to groundwater resources

Global Peak in Atmospheric Radiocarbon Provides a Potential Definition for the Onset of the Anthropocene Epoch in 1965

Anthropogenic activity is now recognised as having profoundly and permanently altered the Earth system, suggesting we have entered a human-dominated geological epoch, the ‘Anthropocene’. To formally define the onset of the Anthropocene, a synchronous global signature within geological-forming materials is required. Here we report a series of precisely-dated tree-ring records from Campbell Island (Southern Ocean) that capture peak atmospheric radiocarbon (14C) resulting from Northern Hemisphere-dominated thermonuclear bomb tests during the 1950s and 1960s. The only alien tree on the island, a Sitka spruce (Picea sitchensis), allows us to seasonally-resolve Southern Hemisphere atmospheric 14C, demonstrating the ‘bomb peak’ in this remote and pristine location occurred in the last-quarter of 1965 (October-December), coincident with the broader changes associated with the post-World War II ‘Great Acceleration’ in industrial capacity and consumption. Our findings provide a precisely-resolved potential Global Stratotype Section and Point (GSSP) or ‘golden spike’, marking the onset of the Anthropocene Epoch

Is analysing the nitrogen use at the plant canopy level a matter of choosing the right optimization criterion?

Optimization theory in combination with canopy modeling is potentially a powerful tool for evaluating the adaptive significance of photosynthesis-related plant traits. Yet its successful application has been hampered by a lack of agreement on the appropriate optimization criterion. Here we review how models based on different types of optimization criteria have been used to analyze traits—particularly N reallocation and leaf area indices—that determine photosynthetic nitrogen-use efficiency at the canopy level. By far the most commonly used approach is static-plant simple optimization (SSO). Static-plant simple optimization makes two assumptions: (1) plant traits are considered to be optimal when they maximize whole-stand daily photosynthesis, ignoring competitive interactions between individuals; (2) it assumes static plants, ignoring canopy dynamics (production and loss of leaves, and the reallocation and uptake of nitrogen) and the respiration of nonphotosynthetic tissue. Recent studies have addressed either the former problem through the application of evolutionary game theory (EGT) or the latter by applying dynamic-plant simple optimization (DSO), and have made considerable progress in our understanding of plant photosynthetic traits. However, we argue that future model studies should focus on combining these two approaches. We also point out that field observations can fit predictions from two models based on very different optimization criteria. In order to enhance our understanding of the adaptive significance of photosynthesis-related plant traits, there is thus an urgent need for experiments that test underlying optimization criteria and competing hypotheses about underlying mechanisms of optimization