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1. Proceedings of the Sixth International Workshop on Climate Informatics: CI 2016

   https://doi.org/10.5065/D6K072N6  

   Eds.: Banerjee, A. ; Ding, W. ; Dy, J. ; Lyubchich, V. ; Rhines, A. ; Series Eds.: Ebert-Uphoff, I. ; Monteleoni, C. ; Nychka, D. ( September 2016 , Climate Informatics Workshop Proceedings)

   Table of Contents: Foreword by the CI 2016 Workshop Chairs …………………………………vi Foreword by the CI 2016 Steering Committee ..…………………………..…..viii List of Organizing Committee ………………………….……....x List of Registered Participants .………………………….……..xi Acknowledgement of Sponsors ……………………………..…xiv Hackathon and Workshop Agenda .………………………………..xv Hackathon Summary .………………………….…..xviii Invited talks - abstracts and links to presentations ………………………………..xxi Proceedings: 34 short research papers ……………………………….. 1-135 Papers 1. BAYESIAN MODELS FOR CLIMATE RECONSTRUCTION FROM POLLEN RECORDS ..................................... 1 Lasse Holmström, Liisa Ilvonen, Heikki Seppä, Siim Veski 2. ON INFORMATION CRITERIA FOR DYNAMIC SPATIO-TEMPORAL CLUSTERING ..................................... 5 Ethan D. Schaeffer, Jeremy M. Testa, Yulia R. Gel, Vyacheslav Lyubchich 3. DETECTING MULTIVARIATE BIOSPHERE EXTREMES ..................................... 9 Yanira Guanche García, Erik Rodner, Milan Flach, Sebastian Sippel, Miguel Mahecha, Joachim Denzler 4. SPATIO-TEMPORAL GENERATIVE MODELS FOR RAINFALL OVER INDIA ..................................... 13 Adway Mitra 5. A NONPARAMETRIC COPULA BASED BIAS CORRECTION METHOD FOR STATISTICAL DOWNSCALING ..................................... 17 Yi Li, Adam Ding, Jennifer Dy 6. DETECTING AND PREDICTING BEAUTIFUL SUNSETS USING SOCIAL MEDIA DATA ..................................... 21 Emma Pierson 7. OCEANTEA: EXPLORING OCEAN-DERIVED CLIMATE DATA USING MICROSERVICES ..................................... 25 Arne N. Johanson, Sascha Flögel, Wolf-Christian Dullo, Wilhelm Hasselbring 8. IMPROVED ANALYSIS OF EARTH SYSTEM MODELS AND OBSERVATIONS USING SIMPLE CLIMATE MODELS ..................................... 29 Balu Nadiga, Nathanmore »Urban 9. SYNERGY AND ANALOGY BETWEEN 15 YEARS OF MICROWAVE SST AND ALONG-TRACK SSH ..................................... 33 Pierre Tandeo, Aitor Atencia, Cristina Gonzalez-Haro 10. PREDICTING EXECUTION TIME OF CLIMATE-DRIVEN ECOLOGICAL FORECASTING MODELS ..................................... 37 Scott Farley and John W. Williams 11. SPATIOTEMPORAL ANALYSIS OF SEASONAL PRECIPITATION OVER US USING CO-CLUSTERING ..................................... 41 Mohammad Gorji–Sefidmazgi, Clayton T. Morrison 12. PREDICTION OF EXTREME RAINFALL USING HYBRID CONVOLUTIONAL-LONG SHORT TERM MEMORY NETWORKS ..................................... 45 Sulagna Gope, Sudeshna Sarkar, Pabitra Mitra 13. SPATIOTEMPORAL PATTERN EXTRACTION WITH DATA-DRIVEN KOOPMAN OPERATORS FOR CONVECTIVELY COUPLED EQUATORIAL WAVES ..................................... 49 Joanna Slawinska, Dimitrios Giannakis 14. COVARIANCE STRUCTURE ANALYSIS OF CLIMATE MODEL OUTPUT ..................................... 53 Chintan Dalal, Doug Nychka, Claudia Tebaldi 15. SIMPLE AND EFFICIENT TENSOR REGRESSION FOR SPATIOTEMPORAL FORECASTING ..................................... 57 Rose Yu, Yan Liu 16. TRACKING OF TROPICAL INTRASEASONAL CONVECTIVE ANOMALIES ..................................... 61 Bohar Singh, James L. Kinter 17. ANALYSIS OF AMAZON DROUGHTS USING SUPERVISED KERNEL PRINCIPAL COMPONENT ANALYSIS ..................................... 65 Carlos H. R. Lima, Amir AghaKouchak 18. A BAYESIAN PREDICTIVE ANALYSIS OF DAILY PRECIPITATION DATA ..................................... 69 Sai K. Popuri, Nagaraj K. Neerchal, Amita Mehta 19. INCORPORATING PRIOR KNOWLEDGE IN SPATIO-TEMPORAL NEURAL NETWORK FOR CLIMATIC DATA ..................................... 73 Arthur Pajot, Ali Ziat, Ludovic Denoyer, Patrick Gallinari 20. DIMENSIONALITY-REDUCTION OF CLIMATE DATA USING DEEP AUTOENCODERS ..................................... 77 Juan A. Saenz, Nicholas Lubbers, Nathan M. Urban 21. MAPPING PLANTATION IN INDONESIA ..................................... 81 Xiaowei Jia, Ankush Khandelwal, James Gerber, Kimberly Carlson, Paul West, Vipin Kumar 22. FROM CLIMATE DATA TO A WEIGHTED NETWORK BETWEEN FUNCTIONAL DOMAINS ..................................... 85 Ilias Fountalis, Annalisa Bracco, Bistra Dilkina, Constantine Dovrolis 23. EMPLOYING SOFTWARE ENGINEERING PRINCIPLES TO ENHANCE MANAGEMENT OF CLIMATOLOGICAL DATASETS FOR CORAL REEF ANALYSIS ..................................... 89 Mark Jenne, M.M. Dalkilic, Claudia Johnson 24. Profiler Guided Manual Optimization for Accelerating Cholesky Decomposition on R Environment ..................................... 93 V.B. Ramakrishnaiah, R.P. Kumar, J. Paige, D. Hammerling, D. Nychka 25. GLOBAL MONITORING OF SURFACE WATER EXTENT DYNAMICS USING SATELLITE DATA ..................................... 97 Anuj Karpatne, Ankush Khandelwal and Vipin Kumar 26. TOWARD QUANTIFYING TROPICAL CYCLONE RISK USING DIAGNOSTIC INDICES .................................... 101 Erica M. Staehling and Ryan E. Truchelut 27. OPTIMAL TROPICAL CYCLONE INTENSITY ESTIMATES WITH UNCERTAINTY FROM BEST TRACK DATA .................................... 105 Suz Tolwinski-Ward 28. EXTREME WEATHER PATTERN DETECTION USING DEEP CONVOLUTIONAL NEURAL NETWORK .................................... 109 Yunjie Liu, Evan Racah, Prabhat, Amir Khosrowshahi, David Lavers, Kenneth Kunkel, Michael Wehner, William Collins 29. INFORMATION TRANSFER ACROSS TEMPORAL SCALES IN ATMOSPHERIC DYNAMICS .................................... 113 Nikola Jajcay and Milan Paluš 30. Identifying precipitation regimes in China using model-based clustering of spatial functional data .................................... 117 Haozhe Zhang, Zhengyuan Zhu, Shuiqing Yin 31. RELATIONAL RECURRENT NEURAL NETWORKS FOR SPATIOTEMPORAL INTERPOLATION FROM MULTI-RESOLUTION CLIMATE DATA .................................... 121 Guangyu Li, Yan Liu 32. OBJECTIVE SELECTION OF ENSEMBLE BOUNDARY CONDITIONS FOR CLIMATE DOWNSCALING .................................... 124 Andrew Rhines, Naomi Goldenson 33. LONG-LEAD PREDICTION OF EXTREME PRECIPITATION CLUSTER VIA A SPATIO-TEMPORAL CONVOLUTIONAL NEURAL NETWORK .................................... 128 Yong Zhuang, Wei Ding 34. MULTIPLE INSTANCE LEARNING FOR BURNED AREA MAPPING USING MULTI –TEMPORAL REFLECTANCE DATA .................................... 132 Guruprasad Nayak, Varun Mithal, Vipin Kumar« less
2. Metabolism of a subtidal rocky mussel reef in a high-temperate setting: pathways of organic C flow

   https://doi.org/10.3354/meps13372  

   Attard, KM ; Rodil, IF ; Berg, P ; Mogg, AOM ; Westerbom, M ; Norkko, A ; Glud, RN ( July 2020 , Marine Ecology Progress Series)

   Mytilid mussels form abundant, species-rich reefs on rocky substrates, but the role of this key habitat in carbon (C) cycling remains poorly understood. We performed a seasonal study on a 5 m deep photic Mytilus trossulus reef in the Central Baltic Sea to investigate pathways and rates of organic C flow. Reef gross primary production (GPP) and respiration ( R ) were estimated seasonally using underwater O 2 eddy covariance on hourly and daily timescales. Photogrammetry and biotic sampling were used to quantify reef rugosity and mussel coverage, and to derive mussel filtration and biodeposition. Mussels were highly abundant, reaching ~50000 ind. m -2 , and the reef structure increased the seabed surface area by 44%. GPP hourly was up to 20 mmol O 2 m -2 h -1 and GPP daily was up to 107 mmol O 2 m -2 d -1 , comparable to a nearby seagrass canopy. Hourly eddy fluxes responded linearly to light intensity and flow velocity, with higher velocities enhancing reef O 2 uptake at night. Reef R daily exceeded GPP daily on 12 of 13 measurement days, and R annual (29 mol O 2 m -2 yr -1 ) was 3-fold larger than GPPmore »annual . The reef sustained a productive community of microbes and fauna whose activities accounted for ~50% of R annual . Horizontal water advection promoted food supply to the reef and likely facilitated substantial lateral C export of mussel biodeposits. Our analyses suggest that a reduction in mussel reef extent due to ongoing environmental change will have major implications for the transport and transformation of C and nutrients within the coastal Baltic Sea.« less
3. High rates of daytime river metabolism are an underestimated component of carbon cycling

   https://doi.org/10.1038/s43247-022-00607-2  

   Tromboni, Flavia ; Hotchkiss, Erin R. ; Schechner, Anne E. ; Dodds, Walter K. ; Poulson, Simon R. ; Chandra, Sudeep ( November 2022 , Communications Earth & Environment)

   River metabolism and, thus, carbon cycling are governed by gross primary production and ecosystem respiration. Traditionally river metabolism is derived from diel dissolved oxygen concentrations, which cannot resolve diel changes in ecosystem respiration. Here, we compare river metabolism derived from oxygen concentrations with estimates from stable oxygen isotope signatures (δ¹⁸O₂) from 14 sites in rivers across three biomes using Bayesian inverse modeling. We find isotopically derived ecosystem respiration was greater in the day than night for all rivers (maximum change of 113 g O₂ m⁻² d⁻¹, minimum of 1 g O₂ m⁻² d⁻¹). Temperature (20 °C) normalized rates of ecosystem respiration and gross primary production were 1.1 to 87 and 1.5 to 22-fold higher when derived from oxygen isotope data compared to concentration data. Through accounting for diel variation in ecosystem respiration, our isotopically-derived rates suggest that ecosystem respiration and microbial carbon cycling in rivers is more rapid than predicted by traditional methods.
4. Uncertainty in land carbon budget simulated by terrestrial biosphere models: the role of atmospheric forcing

   https://doi.org/10.1088/1748-9326/ac888d  

   Hardouin, Lucas ; Delire, Christine ; Decharme, Bertrand ; Lawrence, David M. ; Nabel, Julia E. M. S. ; Brovkin, Victor ; Collier, Nathan ; Fisher, Rosie ; Hoffman, Forrest M. ; Koven, Charles D. ; et al ( September 2022 , Environmental Research Letters)

   Global estimates of the land carbon sink are often based on simulations by terrestrial biosphere models (TBMs). The use of a large number of models that differ in their underlying hypotheses, structure and parameters is one way to assess the uncertainty in the historical land carbon sink. Here we show that the atmospheric forcing datasets used to drive these TBMs represent a significant source of uncertainty that is currently not systematically accounted for in land carbon cycle evaluations. We present results from three TBMs each forced with three different historical atmospheric forcing reconstructions over the period 1850–2015. We perform an analysis of variance to quantify the relative uncertainty in carbon fluxes arising from the models themselves, atmospheric forcing, and model-forcing interactions. We find that atmospheric forcing in this set of simulations plays a dominant role on uncertainties in global gross primary productivity (GPP) (75% of variability) and autotrophic respiration (90%), and a significant but reduced role on net primary productivity and heterotrophic respiration (30%). Atmospheric forcing is the dominant driver (52%) of variability for the net ecosystem exchange flux, defined as the difference between GPP and respiration (both autotrophic and heterotrophic respiration). In contrast, for wildfire-driven carbon emissions modelmore »uncertainties dominate and, as a result, model uncertainties dominate for net ecosystem productivity. At regional scales, the contribution of atmospheric forcing to uncertainty shows a very heterogeneous pattern and is smaller on average than at the global scale. We find that this difference in the relative importance of forcing uncertainty between global and regional scales is related to large differences in regional model flux estimates, which partially offset each other when integrated globally, while the flux differences driven by forcing are mainly consistent across the world and therefore add up to a larger fractional contribution to global uncertainty.

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5. Greenhouse gas balance in global pasturelands and rangelands

   https://doi.org/10.1088/1748-9326/abaa79  

   Dangal, Shree R. S. ; Tian, Hanqin ; Pan, Shufen ; Zhang, Lei ; Xu, Rongting ( September 2020 , Environmental Research Letters)

   Grassland ecosystems play an essential role in climate regulation through carbon (C) storage in plant and soil. But, anthropogenic practices such as livestock grazing, grazing related excreta nitrogen (N) deposition, and manure/fertilizer N application have the potential to reduce the effectiveness of grassland C sink through increased nitrous oxide (N₂O) and methane (CH₄) emissions. Although the effect of anthropogenic activities on net greenhouse gas (GHG) fluxes in grassland ecosystems have been investigated at local to regional scales, estimates of net GHG balance at the global scale remains uncertain. With the data-model framework integrating empirical estimates of livestock CH₄emissions with process-based modeling estimates of land CO₂, N₂O and CH₄fluxes, we examined the overall global warming potential (GWP) of grassland ecosystems during 1961–2010. We then quantified the grassland-specific and regional variations to identify hotspots of GHG fluxes. Our results show that, over a 100-year time horizon, grassland ecosystems sequestered a cumulative total of 113.9 Pg CO₂-eq in plant and soil, but then released 91.9 Pg CO₂-eq to the atmosphere, offsetting 81% of the net CO₂sink. We also found large grassland-specific variations in net GHG fluxes, withpasturelandsacting as a small GHG source of 1.52 ± 143 Tg CO₂-eq yr⁻¹(mean ± 1.0 s.d.)more »andrangelandsa strong GHG sink (−442 ± 266 Tg CO₂-eq yr⁻¹) during 1961–2010. Regionally, Europe acted as a GHG source of 23 ± 10 Tg CO₂-eq yr⁻¹, while other regions (i.e. Africa, Southern Asia) were strong GHG sinks during 2001–2010. Our study highlights the importance of considering regional and grassland-specific differences in GHG fluxes for guiding future management and climate mitigation strategies in global grasslands.

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