Coupled impacts of the diurnal cycle of sea surface temperature on the Madden–Julian oscillation

Author Posting. © American Meteorological Society, 2014. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 27 (2014): 8422–8443, doi:10.1175/JCLI-D-14-00141.1.This study quantifies, from a systematic set of regional ocean–atmosphere coupled model simulations employing various coupling intervals, the effect of subdaily sea surface temperature (SST) variability on the onset and intensity of Madden–Julian oscillation (MJO) convection in the Indian Ocean. The primary effect of diurnal SST variation (dSST) is to raise time-mean SST and latent heat flux (LH) prior to deep convection. Diurnal SST variation also strengthens the diurnal moistening of the troposphere by collocating the diurnal peak in LH with those of SST. Both effects enhance the convection such that the total precipitation amount scales quasi-linearly with preconvection dSST and time-mean SST. A column-integrated moist static energy (MSE) budget analysis confirms the critical role of diurnal SST variability in the buildup of column MSE and the strength of MJO convection via stronger time-mean LH and diurnal moistening. Two complementary atmosphere-only simulations further elucidate the role of SST conditions in the predictive skill of MJO. The atmospheric model forced with the persistent initial SST, lacking enhanced preconvection warming and moistening, produces a weaker and delayed convection than the diurnally coupled run. The atmospheric model with prescribed daily-mean SST from the coupled run, while eliminating the delayed peak, continues to exhibit weaker convection due to the lack of strong moistening on a diurnal basis. The fact that time-evolving SST with a diurnal cycle strongly influences the onset and intensity of MJO convection is consistent with previous studies that identified an improved representation of diurnal SST as a potential source of MJO predictability.The authors gratefully acknowledge support from the Office of Naval Research (N00014-13-1-0133 and N00014-13-1-0139) and National Science Foundation EaSM-3 (OCE-1419235). HS especially thanks the Penzance Endowed Fund for their support of Assistant Scientists at WHOI.2015-05-1

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some existing stochastic parameterizations utilize data-driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and sub-grid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate timescales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both timescales, and the networks closely reproduce the spatio-temporal correlations and regimes of the Lorenz '96 system. We also find that in general those models which produce skillful forecasts are also associated with the best climate simulations.Comment: Submitted to Journal of Advances in Modeling Earth Systems (JAMES

A Reliability Budget Analysis of CESM-DART

A reliability budget is used to diagnose potential sources of error (departure from observations) in a new prototype coupled ocean-atmosphere ensemble Kalman filter reanalysis product, the Community Earth System Model using the Data Assimilation Research Testbed (CESM-DART). In areas with sufficient observations, the mean bias in zonal wind was generally very low compared to the spread due to ensemble variance, which did not exhibit patterns associated with Northern Hemisphere jet streams but did have regional enhancement over the Maritime Continent. However, the residual term was often the largest contributor to the budget, which is problematic, suggesting improper observational error statistics and inadequately represented ensemble variance statistics. The departure and residual exhibit significant seasonal variability, with a strong peak in boreal winter months, indicating the model's deficiencies during the energetic Northern Hemisphere winter. Ocean temperature contained large error in areas with eddy production indicating inadequate ensemble variance due to poor model resolution. Periods when the Madden-Julian Oscillation (MJO) was active exhibited lower error, especially in the western equatorial Pacific during MJO phases with reduced convection. In contrast, during MJO phases with enhanced convection in that region, the ensemble variance is increased yet the error is comparable to non-MJO conditions, suggesting a controlling effect of the precipitation parameterization. Further studies evaluating the impact of the coupled assimilation procedure on the reliability budget will be illuminating. </div

The role of air-sea interactions in atmospheric rivers: Case studies using the SKRIPS regional coupled model

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sun, R., Subramanian, A. C., Cornuelle, B. D., Mazloff, M. R., Miller, A. J., Ralph, F. M., Seo, H., & Hoteit, I. The role of air-sea interactions in atmospheric rivers: Case studies using the SKRIPS regional coupled model. Journal of Geophysical Research: Atmospheres, 126(6), (2021): e2020JD032885, https://doi.org/10.1029/2020JD032885.Atmospheric rivers (ARs) play a key role in California's water supply and are responsible for most of the extreme precipitation and major flooding along the west coast of North America. Given the high societal impact, it is critical to improve our understanding and prediction of ARs. This study uses a regional coupled ocean–atmosphere modeling system to make hindcasts of ARs up to 14 days. Two groups of coupled runs are highlighted in the comparison: (1) ARs occurring during times with strong sea surface temperature (SST) cooling and (2) ARs occurring during times with weak SST cooling. During the events with strong SST cooling, the coupled model simulates strong upward air–sea heat fluxes associated with ARs; on the other hand, when the SST cooling is weak, the coupled model simulates downward air–sea heat fluxes in the AR region. Validation data shows that the coupled model skillfully reproduces the evolving SST, as well as the surface turbulent heat transfers between the ocean and atmosphere. The roles of air–sea interactions in AR events are investigated by comparing coupled model hindcasts to hindcasts made using persistent SST. To evaluate the influence of the ocean on ARs we analyze two representative variables of AR intensity, the vertically integrated water vapor (IWV) and integrated vapor transport (IVT). During strong SST cooling AR events the simulated IWV is improved by about 12% in the coupled run at lead times greater than one week. For IVT, which is about twice more variable, the improvement in the coupled run is about 5%.The authors gratefully acknowledge the research funding (grant number: OSR-2-16-RPP-3268.02) from KAUST (King Abdullah University of Science and Technology). The authors also appreciate the computational resources on supercomputer Shaheen II and the assistance provided by KAUST Supercomputer Laboratory. Additional funding from the NSF (OCE2022846, and OCE2022868) and the National Oceanic and Atmospheric Administration (MAPP NA17OAR4310106 and NA17OAR4310255) is also greatly appreciated. This study is also supported by the U.S. Army Corps of Engineers (USACE)-Cooperative Ecosystem Studies Unit (CESU) as part of Forecast Informed Reservoir Operations (FIRO) under grant W912HZ-15-2-0019. The authors thank Caroline Papadopoulos for important technical support when installing software and using the Shaheen II cluster

Predictability of US West Coast Ocean Temperatures is not solely due to ENSO

The causes of the extreme and persistent warming in the Northeast Pacific from the winter of 2013/14 to that of 2014/15 are still not fully understood. While global warming may have contributed, natural influences may also have played a role. El Ni&ntilde;o events are often implicated in anomalously warm conditions along the US West Coast (USWC). However, the tropical Pacific sea surface temperature (SST) anomalies were generally weak during 2014, calling into question their role in the USWC warming. In this study, we identify tropical Pacific &ldquo;sensitivity patterns&rdquo; that optimally force USWC warming at a later time. We find that such sensitivity patterns do not coincide with the mature SST anomaly patterns usually associated with ENSO, but instead include elements associated with ENSO SST precursors and SST anomalies in the central/western equatorial Pacific. El Ni&ntilde;o events that produce large USWC warming, irrespective of their magnitude, do project on the sensitivity pattern and are characterized by a distinct evolution of the North Pacific atmospheric and oceanic fields. However, even weak tropical SST anomalies in the right location, and not necessarily associated with ENSO, can significantly influence USWC conditions and enhance their predictability.</p

Towards implementing artificial intelligence post-processing in weather and climate: Proposed actions from the Oxford 2019 workshop

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results

A road map to IndOOS-2 better observations of the rapidly warming Indian Ocean

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 101(11), (2020): E1891-E1913, https://doi.org/10.1175/BAMS-D-19-0209.1The Indian Ocean Observing System (IndOOS), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific challenges, a 3-yr international review of the IndOOS by more than 60 scientific experts now highlights the need for an enhanced observing network that can better meet societal challenges, and provide more reliable forecasts. Here we present core findings from this review, including the need for 1) chemical, biological, and ecosystem measurements alongside physical parameters; 2) expansion into the western tropics to improve understanding of the monsoon circulation; 3) better-resolved upper ocean processes to improve understanding of air–sea coupling and yield better subseasonal to seasonal predictions; and 4) expansion into key coastal regions and the deep ocean to better constrain the basinwide energy budget. These goals will require new agreements and partnerships with and among Indian Ocean rim countries, creating opportunities for them to enhance their monitoring and forecasting capacity as part of IndOOS-2.We thank the World Climate Research Programme (WCRP) and its core project on Climate and Ocean: Variability, Predictability and Change (CLIVAR), the Indian Ocean Global Ocean Observing System (IOGOOS), the Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO), the Integrated Marine Biosphere Research (IMBeR) project, the U.S. National Oceanic and Atmospheric Administration (NOAA), and the International Union of Geodesy and Geophysics (IUGG) for providing the financial support to bring international scientists together to conduct this review. We thank the members of the independent review board that provided detailed feedbacks on the review report that is summarized in this article: P. E. Dexter, M. Krug, J. McCreary, R. Matear, C. Moloney, and S. Wijffels. PMEL Contribution 5041. C. Ummenhofer acknowledges support from The Andrew W. Mellon Foundation Award for Innovative Research.2021-05-0

Observational needs for improving ocean and coupled reanalysis, S2S prediction, and decadal prediction

Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network

A sustained ocean observing system in the Indian Ocean for climate related scientific knowledge and societal needs

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hermes, J. C., Masumoto, Y., Beal, L. M., Roxy, M. K., Vialard, J., Andres, M., Annamalai, H., Behera, S., D'Adamo, N., Doi, T., Peng, M., Han, W., Hardman-Mountford, N., Hendon, H., Hood, R., Kido, S., Lee, C., Lees, T., Lengaigne, M., Li, J., Lumpkin, R., Navaneeth, K. N., Milligan, B., McPhaden, M. J., Ravichandran, M., Shinoda, T., Singh, A., Sloyan, B., Strutton, P. G., Subramanian, A. C., Thurston, S., Tozuka, T., Ummenhofer, C. C., Unnikrishnan, A. S., Venkatesan, R., Wang, D., Wiggert, J., Yu, L., & Yu, W. (2019). A sustained ocean observing system in the Indian Ocean for climate related scientific knowledge and societal needs. Frontiers in Marine Science, 6, (2019): 355, doi: 10.3389/fmars.2019.00355.The Indian Ocean is warming faster than any of the global oceans and its climate is uniquely driven by the presence of a landmass at low latitudes, which causes monsoonal winds and reversing currents. The food, water, and energy security in the Indian Ocean rim countries and islands are intrinsically tied to its climate, with marine environmental goods and services, as well as trade within the basin, underpinning their economies. Hence, there are a range of societal needs for Indian Ocean observation arising from the influence of regional phenomena and climate change on, for instance, marine ecosystems, monsoon rains, and sea-level. The Indian Ocean Observing System (IndOOS), is a sustained observing system that monitors basin-scale ocean-atmosphere conditions, while providing flexibility in terms of emerging technologies and scientificand societal needs, and a framework for more regional and coastal monitoring. This paper reviews the societal and scientific motivations, current status, and future directions of IndOOS, while also discussing the need for enhanced coastal, shelf, and regional observations. The challenges of sustainability and implementation are also addressed, including capacity building, best practices, and integration of resources. The utility of IndOOS ultimately depends on the identification of, and engagement with, end-users and decision-makers and on the practical accessibility and transparency of data for a range of products and for decision-making processes. Therefore we highlight current progress, issues and challenges related to end user engagement with IndOOS, as well as the needs of the data assimilation and modeling communities. Knowledge of the status of the Indian Ocean climate and ecosystems and predictability of its future, depends on a wide range of socio-economic and environmental data, a significant part of which is provided by IndOOS.This work was supported by the PMEL contribution no. 4934

Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jacox, M. G., Alexander, M. A., Siedlecki, S., Chen, K., Kwon, Y., Brodie, S., Ortiz, I., Tommasi, D., Widlansky, M. J., Barrie, D., Capotondi, A., Cheng, W., Di Lorenzo, E., Edwards, C., Fiechter, J., Fratantoni, P., Hazen, E. L., Hermann, A. J., Kumar, A., Miller, A. J., Pirhalla, D., Buil, M. P., Ray, S., Sheridan, S. C., Subramanian, A., Thompson, P., Thorne, L., Annamalai, H., Aydin, K., Bograd, S. J., Griffis, R. B., Kearney, K., Kim, H., Mariotti, A., Merrifield, M., & Rykaczewski, R. Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments. Progress in Oceanography, 183, (2020): 102307, doi:10.1016/j.pocean.2020.102307.Marine ecosystem forecasting is an area of active research and rapid development. Promise has been shown for skillful prediction of physical, biogeochemical, and ecological variables on a range of timescales, suggesting potential for forecasts to aid in the management of living marine resources and coastal communities. However, the mechanisms underlying forecast skill in marine ecosystems are often poorly understood, and many forecasts, especially for biological variables, rely on empirical statistical relationships developed from historical observations. Here, we review statistical and dynamical marine ecosystem forecasting methods and highlight examples of their application along U.S. coastlines for seasonal-to-interannual (1–24 month) prediction of properties ranging from coastal sea level to marine top predator distributions. We then describe known mechanisms governing marine ecosystem predictability and how they have been used in forecasts to date. These mechanisms include physical atmospheric and oceanic processes, biogeochemical and ecological responses to physical forcing, and intrinsic characteristics of species themselves. In reviewing the state of the knowledge on forecasting techniques and mechanisms underlying marine ecosystem predictability, we aim to facilitate forecast development and uptake by (i) identifying methods and processes that can be exploited for development of skillful regional forecasts, (ii) informing priorities for forecast development and verification, and (iii) improving understanding of conditional forecast skill (i.e., a priori knowledge of whether a forecast is likely to be skillful). While we focus primarily on coastal marine ecosystems surrounding North America (and the U.S. in particular), we detail forecast methods, physical and biological mechanisms, and priority developments that are globally relevant.This study was supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) program through grants NA17OAR4310108, NA17OAR4310112, NA17OAR4310111, NA17OAR4310110, NA17OAR4310109, NA17OAR4310104, NA17OAR4310106, and NA17OAR4310113. This paper is a product of the NOAA/MAPP Marine Prediction Task Force