Description: Author Posting. © American Meteorological Society, 2007. 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 20 (2007): 2760-2768, doi:10.1175/JCLI4138a.1

Description: The correlation between parameters characterizing observed westerly wind bursts (WWBs) in the equatorial Pacific and the large-scale SST is analyzed using singular value decomposition. The WWB parameters include the amplitude, location, scale, and probability of occurrence for a given SST distribution rather than the wind stress itself. This approach therefore allows for a nonlinear relationship between the SST and the wind signal of the WWBs. It is found that about half of the variance of the WWB parameters is explained by only two large-scale SST modes. The first mode represents a developed El Niño event, while the second mode represents the seasonal cycle. More specifically, the central longitude of WWBs, their longitudinal extent, and their probability seem to be determined to a significant degree by the ENSO-driven signal. The amplitude of the WWBs is found to be strongly influenced by the phase of the seasonal cycle. It is concluded that the WWBs, while partially stochastic, seem an inherent part of the large-scale deterministic ENSO dynamics. Implications for ENSO predictability and prediction are discussed.

Description: Eli Tziperman is supported by the U.S. National Science Foundation Climate Dynamics Program Grant ATM- 0351123 and by the McDonnell Foundation. Lisan Yu is supported by the NASA Ocean Vector Wind Science Team under JPL Contract 1216955 and NSF Climate Dynamics Grant ATM-0350266.

Description: Author Posting. © American Meteorological Society, 2011. 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 Physical Oceanography 41 (2011): 1741–1755, doi:10.1175/2011JPO4437.1.

Description: An in-depth data analysis was conducted to understand the occurrence of a strong sea surface temperature (SST) front in the central Bay of Bengal before the formation of Cyclone Nargis in April 2008. Nargis changed its course after encountering the front and tracked along the front until making landfall. One unique feature of this SST front was its coupling with high sea surface height anomalies (SSHAs), which is unusual for a basin where SST is normally uncorrelated with SSHA. The high SSHAs were associated with downwelling Rossby waves, and the interaction between downwelling and surface fresh waters was a key mechanism to account for the observed SST–SSHA coupling. The near-surface salinity field in the bay is characterized by strong stratification and a pronounced horizontal gradient, with low salinity in the northeast. During the passage of downwelling Rossby waves, freshening of the surface layer was observed when surface velocities were southwestward. Horizontal convergence of freshwater associated with downwelling Rossby waves increased the buoyancy of the upper layer and caused the mixed layer to shoal to within a few meters of the surface. Surface heating trapped in the thin mixed layer caused the fresh layer to warm, whereas the increase in buoyancy from low-salinity waters enhanced the high SSHA associated with Rossby waves. Thus, high SST coincided with high SSHA. The dominant role of salinity in controlling high SSHA suggests that caution should be exercised when computing hurricane heat potential in the bay from SSHA. This situation is different from most tropical oceans, where temperature has the dominant effect on SSHA.

Description: This work was supported by the NOAA/Office of Climate Observation (OCO) program.

Description: Author Posting. © American Meteorological Society, 2017. 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 30 (2017): 3829-3852, doi:10.1175/JCLI-D-16-0479.1.

Description: 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.

Description: Primary support for the study is provided by the NOAAModeling, Analysis, Predictions, and Projections (MAPP) Program’s Climate Reanalysis Task Force (CRTF) through Grant NA13OAR4310106.

Description: 2017-11-02

Description: Author Posting. © American Meteorological Society 2006. 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 19 (2006): 6153–6169, doi:10.1175/JCLI3970.1.

Description: The present study used a new net surface heat flux (Qnet) product obtained from the Objective Analyzed Air–Sea Fluxes (OAFlux) project and the International Satellite Cloud Climatology Project (ISCCP) to examine two specific issues—one is to which degree Qnet controls seasonal variations of sea surface temperature (SST) in the tropical Atlantic Ocean (20°S–20°N, east of 60°W), and the other is whether the physical relation can serve as a measure to evaluate the physical representation of a heat flux product. To better address the two issues, the study included the analysis of three additional heat flux products: the Southampton Oceanographic Centre (SOC) heat flux analysis based on ship reports, and the model fluxes from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). The study also uses the monthly subsurface temperature fields from the World Ocean Atlas to help analyze the seasonal changes of the mixed layer depth (hMLD). The study showed that the tropical Atlantic sector could be divided into two regimes based on the influence level of Qnet. SST variability poleward of 5°S and 10°N is dominated by the annual cycle of Qnet. In these regions the warming (cooling) of the sea surface is highly correlated with the increased (decreased) Qnet confined in a relatively shallow (deep) hMLD. The seasonal evolution of SST variability is well predicted by simply relating the local Qnet with a variable hMLD. On the other hand, the influence of Qnet diminishes in the deep Tropics within 5°S and 10°N and ocean dynamic processes play a dominant role. The dynamics-induced changes in SST are most evident along the two belts, one of which is located on the equator and the other off the equator at about 3°N in the west, which tilts to about 10°N near the northwestern African coast. The study also showed that if the degree of consistency between the correlation relationships of Qnet, hMLD, and SST variability serves as a measure of the quality of the Qnet product, then the Qnet from OAFlux + ISCCP and ERA-40 are most physically representative, followed by SOC. The NCEP–NCAR Qnet is least representative. It should be noted that the Qnet from OAFlux + ISCCP and ERA-40 have a quite different annual mean pattern. OAFlux + ISCCP agrees with SOC in that the tropical Atlantic sector gains heat from the atmosphere on the annual mean basis, where the ERA-40 and the NCEP–NCAR model reanalyses indicate that positive Qnet occurs only in the narrow equatorial band and in the eastern portion of the tropical basin. Nevertheless, seasonal variances of the Qnet from OAFlux + ISCCP and ERA-40 are very similar once the respective mean is removed, which explains why the two agree with each other in accounting for the seasonal variability of SST. In summary, the study suggests that an accurate estimation of surface heat flux is crucially important for understanding and predicting SST fluctuations in the tropical Atlantic Ocean. It also suggests that future emphasis on improving the surface heat flux estimation should be placed more on reducing the mean bias.

Description: This study is support by the NOAA CLIVAR Atlantic under Grant NA06GP0453 and NOAA Climate observations and Climate Change and Data Detection under Grant NA17RJ1223.

Description: Author Posting. © American Meteorological Society, 2021. 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 102(10), (2021): E1897–E1935, https://doi.org/10.1175/BAMS-D-19-0316.1.

Description: Life on Earth vitally depends on the availability of water. Human pressure on freshwater resources is increasing, as is human exposure to weather-related extremes (droughts, storms, floods) caused by climate change. Understanding these changes is pivotal for developing mitigation and adaptation strategies. The Global Climate Observing System (GCOS) defines a suite of essential climate variables (ECVs), many related to the water cycle, required to systematically monitor Earth’s climate system. Since long-term observations of these ECVs are derived from different observation techniques, platforms, instruments, and retrieval algorithms, they often lack the accuracy, completeness, and resolution, to consistently characterize water cycle variability at multiple spatial and temporal scales. Here, we review the capability of ground-based and remotely sensed observations of water cycle ECVs to consistently observe the hydrological cycle. We evaluate the relevant land, atmosphere, and ocean water storages and the fluxes between them, including anthropogenic water use. Particularly, we assess how well they close on multiple temporal and spatial scales. On this basis, we discuss gaps in observation systems and formulate guidelines for future water cycle observation strategies. We conclude that, while long-term water cycle monitoring has greatly advanced in the past, many observational gaps still need to be overcome to close the water budget and enable a comprehensive and consistent assessment across scales. Trends in water cycle components can only be observed with great uncertainty, mainly due to insufficient length and homogeneity. An advanced closure of the water cycle requires improved model–data synthesis capabilities, particularly at regional to local scales.

Description: WD acknowledges ESA’s QA4EO (ISMN) and CCI Soil Moisture projects. WD, CRV, AG, and KL acknowledge the G3P project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement 870353. MIH and MS acknowledge ESA’s CCI Water Vapour project. MS and RH acknowledges the support by the EUMETSAT member states through CM SAF. DGM acknowledges support from the European Research Council (ERC) under Grant Agreement 715254 (DRY–2–DRY). Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).

Description: 2022-04-01