Kumar Arun

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Kumar
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Arun
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Article

The global ocean water cycle in atmospheric reanalysis, satellite, and ocean salinity

2017-05-02 , Yu, Lisan , Jin, Xiangze , Josey, Simon A. , Lee, Tong , Kumar, Arun , Wen, Caihong , Xue, Yan

This study provides an assessment of the uncertainty in ocean surface (OS) freshwater budgets and variability using evaporation E and precipitation P from 10 atmospheric reanalyses, two combined satellite-based E − P products, and two observation-based salinity products. Three issues are examined: the uncertainty level in the OS freshwater budget in atmospheric reanalyses, the uncertainty structure and association with the global ocean wet/dry zones, and the potential of salinity in ascribing the uncertainty in E − P. The products agree on the global mean pattern but differ considerably in magnitude. The OS freshwater budgets are 129 ± 10 (8%) cm yr−1 for E, 118 ± 11 (9%) cm yr−1 for P, and 11 ± 4 (36%) cm yr−1 for E − P, where the mean and error represent the ensemble mean and one standard deviation of the ensemble spread. The E − P uncertainty exceeds the uncertainty in E and P by a factor of 4 or more. The large uncertainty is attributed to P in the tropical wet zone. Most reanalyses tend to produce a wider tropical rainband when compared to satellite products, with the exception of two recent reanalyses that implement an observation-based correction for the model-generated P over land. The disparity in the width and the extent of seasonal migrations of the tropical wet zone causes a large spread in P, implying that the tropical moist physics and the realism of tropical rainfall remain a key challenge. Satellite salinity appears feasible to evaluate the fidelity of E − P variability in three tropical areas, where the uncertainty diagnosis has a global indication.

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Preprint

How do uncertainties in NCEP R2 and CFSR surface fluxes impact tropical ocean simulations?

2016-12 , Wen, Caihong , Xue, Yan , Kumar, Arun , Behringer, David W. , Yu, Lisan

NCEP/DOE reanalysis (R2) and Climate Forecast System Reanalysis (CFSR) surface fluxes are widely used by the research community to understand surface flux climate variability, and to drive ocean models as surface forcings. However, large discrepancies exist between these two products, including (1) stronger trade winds in CFSR than in R2 over the tropical Pacific prior 2000; (2) excessive net surface heat fluxes into ocean in CFSR than in R2 with an increase in difference after 2000. The goals of this study are to examine the sensitivity of ocean simulations to discrepancies between CFSR and R2 surface fluxes, and to assess the fidelity of the two products. A set of experiments, where an ocean model was driven by a combination of surface flux component from R2 and CFSR, were carried out. The model simulations were contrasted to identify sensitivity to different component of the surface fluxes in R2 and CFSR. The accuracy of the model simulations was validated against the tropical moorings data, altimetry SSH and SST reanalysis products. Sensitivity of ocean simulations showed that temperature bias difference in the upper 100m is mostly sensitive to the differences in surface heat fluxes, while depth of 20°C (D20) bias difference is mainly determined by the discrepancies in momentum fluxes. D20 simulations with CFSR winds agree with observation well in the western equatorial Pacific prior 2000, but have large negative bias similar to those with R2 winds after 2000, partly because easterly winds over the central Pacific were underestimated in both CFSR and R2. On the other hand, the observed temperature variability is well reproduced in the tropical Pacific by simulations with both R2 and CFSR fluxes. Relative to the R2 fluxes, the CFSR fluxes improve simulation of interannual variability in all three tropical oceans to a varying degree. The improvement in the tropical Atlantic is most significant and is largely attributed to differences in surface winds.

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Article

Ocean observations to improve our understanding, modeling, and forecasting of subseasonal-to-seasonal variability

2019-08-08 , Subramanian, Aneesh C. , Balmaseda, Magdalena A. , Centurioni, Luca R. , Chattopadhyay, Rajib , Cornuelle, Bruce D. , DeMott, Charlotte , Flatau, Maria , Fujii, Yosuke , Giglio, Donata , Gille, Sarah T. , Hamill, Thomas M. , Hendon, Harry , Hoteit, Ibrahim , Kumar, Arun , Lee, Jae-Hak , Lucas, Andrew J. , Mahadevan, Amala , Matsueda, Mio , Nam, SungHyun , Paturi, Shastri , Penny, Stephen G. , Rydbeck, Adam , Sun, Rui , Takaya, Yuhei , Tandon, Amit , Todd, Robert E. , Vitart, Frederic , Yuan, Dongliang , Zhang, Chidong

Subseasonal-to-seasonal (S2S) forecasts have the potential to provide advance information about weather and climate events. The high heat capacity of water means that the subsurface ocean stores and re-releases heat (and other properties) and is an important source of information for S2S forecasts. However, the subsurface ocean is challenging to observe, because it cannot be measured by satellite. Subsurface ocean observing systems relevant for understanding, modeling, and forecasting on S2S timescales will continue to evolve with the improvement in technological capabilities. The community must focus on designing and implementing low-cost, high-value surface and subsurface ocean observations, and developing forecasting system capable of extracting their observation potential in forecast applications. S2S forecasts will benefit significantly from higher spatio-temporal resolution data in regions that are sources of predictability on these timescales (coastal, tropical, and polar regions). While ENSO has been a driving force for the design of the current observing system, the subseasonal time scales present new observational requirements. Advanced observation technologies such as autonomous surface and subsurface profiling devices as well as satellites that observe the ocean-atmosphere interface simultaneously can lead to breakthroughs in coupled data assimilation (CDA) and coupled initialization for S2S forecasts. These observational platforms should also be tested and evaluated in ocean observation sensitivity experiments with current and future generation CDA and S2S prediction systems. Investments in the new ocean observations as well as model and DA system developments can lead to substantial returns on cost savings from disaster mitigation as well as socio–economic decisions that use S2S forecast information.

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Article

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

2020-02-20 , Jacox, Michael , Alexander, Michael A. , Siedlecki, Samantha A. , Chen, Ke , Kwon, Young-Oh , Brodie, Stephanie , Ortiz, Ivonne , Tommasi, Desiree , Widlansky, Matthew J. , Barrie, Daniel , Capotondi, Antonietta , Cheng, Wei , Di Lorenzo, Emanuele , Edwards, Christopher , Fiechter, Jerome , Fratantoni, Paula S. , Hazen, Elliott L. , Hermann, Albert J. , Kumar, Arun , Miller, Arthur J. , Pirhalla, Douglas , Pozo Buil, Mercedes , Ray, Sulagna , Sheridan, Scott , Subramanian, Aneesh C. , Thompson, Philip , Thorne, Lesley , Annamalai, Hariharasubramanian , Aydin, Kerim , Bograd, Steven , Griffis, Roger B. , Kearney, Kelly , Kim, Hyemi , Mariotti, Annarita , Merrifield, Mark , Rykaczewski, Ryan R.

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.

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