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1. Thermodynamic and Dynamic Mechanisms for Hydrological Cycle Intensification over the Full Probability Distribution of Precipitation Events

   https://doi.org/10.1175/JAS-D-18-0067.1  

   Chen, Gang ; Norris, Jesse ; Neelin, J. David ; Lu, Jian ; Leung, L. Ruby ; Sakaguchi, Koichi ( February 2019 , Journal of the Atmospheric Sciences)

   Precipitation changes in a warming climate have been examined with a focus on either mean precipitation or precipitation extremes, but changes in the full probability distribution of precipitation have not been well studied. This paper develops a methodology for the quantile-conditional column moisture budget of the atmosphere for the full probability distribution of precipitation. Analysis is performed on idealized aquaplanet model simulations under 3-K uniform SST warming across different horizontal resolutions. Because the covariance of specific humidity and horizontal mass convergence is much reduced when conditioned onto a given precipitation percentile range, their conditional averages yield a clear separation between the moisture (thermodynamic) and circulation (dynamic) effects of vertical moisture transport on precipitation. The thermodynamic response to idealized climate warming can be understood as a generalized “wet get wetter” mechanism, in which the heaviest precipitation of the probability distribution is enhanced most from increased gross moisture stratification, at a rate controlled by the change in lower-tropospheric moisture rather than column moisture. The dynamic effect, in contrast, can be interpreted by shifts in large-scale atmospheric circulations such as the Hadley cell circulation or midlatitude storm tracks. Furthermore, horizontal moisture advection, albeit of secondary role, is important for regional precipitation change. Althoughmore »similar mechanisms are at play for changes in both mean precipitation and precipitation extremes, the thermodynamic contributions of moisture transport to increases in high percentiles of precipitation tend to be more widespread across a wide range of latitudes than increases in the mean, especially in the subtropics.

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2. Importance of Laplacian of Low-Level Warming for the Response of Precipitation to Climate Change over Tropical Oceans

   https://doi.org/10.1175/JCLI-D-19-0365.1  

   Duffy, Margaret L. ; O’Gorman, Paul A. ; Back, Larissa E. ( May 2020 , Journal of Climate)

   Several physical mechanisms have been proposed for projected changes in mean precipitation in the tropics under climate warming. In particular, the “wet-get-wetter” mechanism describes an amplification of the pattern of precipitation in a moister atmosphere, and the “warmer-get-wetter” mechanism describes enhanced upward motion and precipitation in regions where the increase in SST exceeds the tropical-mean increase. Studies of the current climate have shown that surface convergence over the tropical oceans is largely driven by horizontal gradients of low-level temperature, but the influence of these gradients on the precipitation response under climate warming has received little attention. Here, a simple model is applied to give a decomposition of changes in precipitation over tropical oceans in twenty-first-century climate model projections. The wet-get-wetter mechanism and changes in surface convergence are found to be of widespread importance, whereas the warmer-get-wetter mechanism is primarily limited to negative anomalies in the tropical southern Pacific. Furthermore, surface convergence is linked to gradients of boundary layer temperature using an atmospheric mixed layer model. Changes in surface convergence are found to be strongly related to changes in the Laplacian of boundary layer virtual temperature, and, to a lesser extent, the Laplacian of SST. Taken together, these results suggest thatmore »a “Laplacian-of-warming” mechanism is of comparable importance to wet get wetter and warmer get wetter for the response of precipitation to climate change over tropical oceans.

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3. 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
4. Subseasonal convection variability over the Intra‐American Seas simulated by an AGCM and sensitivity to CMIP5 SST biases and projections

   https://doi.org/https://doi.org/10.1002/joc.6475  

   Vigaud, N ( January 2020 , International journal of climatology)

   The influence of coupled model sea surface temperature (SST) climatological biases and SST projections on daily convection over the Intra-American Seas (IAS) during the May–November rainy season are examined by clustering (k − means) daily OLR anomalies in ECHAM5 atmospheric global climate model (AGCM) experiments. The AGCM is first forced by 1980–2005 observed SSTs (GOGA), then with climatological, multi-model mean monthly climatological SST bias from 31 CMIP5 coupled models (HIST) and projected SST changes for 2040–2059 (PROJ) and 2080–2099 (PROJ2) imposed on top of observed values. A typology of seven recurrent convection regimes is identified and consists of three dry and four wet regimes, including three regimes characterized by tropical-midlatitude interactions between surface convection cells across the IAS and Rossby wave in the upper-troposphere, and a regime of broad wettening typical of the ITCZ. Compared to an earlier observational study, all seven regimes are reasonably well reproduced in the HIST runs. However, the latter exhibit drier dry regimes, a less wet ITCZ-like wet regime and a southeastward shift of convective anomalies developing across the IAS in the three other regimes, all result in a drier simulated IAS climate compared to GOGA. ECHAM5 projection runs for PROJ and PROJ2 are bothmore »characterized by the inhibition of the broad ITCZ-like wet regime, indicating a significant trend towards more frequent dry weather. Meanwhile, convection anomalies related to tropical-midlatitude interactions are shifted further east of the Caribbean as lead increases. These results suggest more frequent and intense extreme rainfall over the tropical Atlantic and the southeast US, while parts of the Caribbean are projected to experience drier climate. The projected drying, however, is of the same order of magnitude as results from historical SST biases, suggesting that the latter need to be considered in model projections, which might underestimate future IAS drying.« less
5. Global Warming Pattern Formation: The Role of Ocean Heat Uptake

   https://doi.org/10.1175/JCLI-D-21-0317.1  

   Hu, Shineng ; Xie, Shang-Ping ; Kang, Sarah M. ( March 2022 , Journal of Climate)

   This study investigates the formation mechanism of the ocean surface warming pattern in response to a doubling CO₂with a focus on the role of ocean heat uptake (or ocean surface heat flux change, ΔQ_net). We demonstrate that thetransientpatterns of surface warming and rainfall change simulated by the dynamic ocean–atmosphere coupled model (DOM) can be reproduced by theequilibriumsolutions of the slab ocean–atmosphere coupled model (SOM) simulations when forced with the DOM ΔQ_netdistribution. The SOM is then used as a diagnostic inverse modeling tool to decompose the CO₂-induced thermodynamic warming effect and the ΔQ_net(ocean heat uptake)–induced cooling effect. As ΔQ_netis largely positive (i.e., downward into the ocean) in the subpolar oceans and weakly negative at the equator, its cooling effect is strongly polar amplified and opposes the CO₂warming, reducing the net warming response especially over Antarctica. For the same reason, the ΔQ_net-induced cooling effect contributes significantly to the equatorially enhanced warming in all three ocean basins, while the CO₂warming effect plays a role in the equatorial warming of the eastern Pacific. The spatially varying component of ΔQ_net, although globally averaged to zero, can effectively rectify and lead to decreased global mean surface temperature of a comparable magnitude as the global meanmore »ΔQ_neteffect under transient climate change. Our study highlights the importance of air–sea interaction in the surface warming pattern formation and the key role of ocean heat uptake pattern.

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