Browsing by Author "Nielsen-Gammon, John"

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ENSO Indices For a Changing Climate
Atmospheric Sciences; TAMU; https://hdl.handle.net/20.500.14641/453; DOC-National Oceanic and Atmospheric Administration
Many years before a basic dynamical understanding of the El Niño-Southern Oscillation (ENSO) was available, ENSO was monitored using the Southern Oscillation Index, which was based on differences of air pressure across the offequatorial Pacific. Later, as the fundamental role of equatorial ocean temperatures was realized, ENSO monitoring and quantification focused on the ocean surface. At present, the most commonly used ENSO indices are based on sea surface temperature (SST) anomalies in fixed regions of the tropical ocean. NOAA’s operational definition of ENSO is based on the Optimal Niño Index (ONI), a running mean of anomalies in the NINO 3.4 region. Many other indices are in use, including those that attempt to quantify different variants of ENSO or those that combine atmospheric and oceanic information. There are at least two problems with this situation. First, the research community presently does not have a standard definition for the intensity of ENSO. Even a researcher who chooses to use a common index, such as NINO 3.4, must decide on the base period from which anomalies are calculated. NOAA has attempted to keep up with climate change by updating its 30-year reference period every five years, but this is a patch rather than a cure. Researchers working with climate model output have chosen to take anomalies relative to fixed periods, to linearly detrended SSTs, to quadratically detrended SSTs, and so forth. The second problem is a more fundamental one. ENSO’s atmospheric impact involves a shift in the location of tropical convection across the tropical Pacific basin. This shift is driven by a reduction or reversal of the zonal sea surface temperature (SST) gradient. The atmospheric response is nonlinear, as a large horizontal displacement of tropical convection is possible once the SST gradient becomes flat or reverses. In a changing climate, the mean zonal SST gradient is likely to change, and so is the magnitude of the NINO 3.4 SST anomaly needed to cause an SST gradient reversal. And in climate models with different tropical SST climatologies, the threshold for SST gradient reversal is model-dependent. A regional anomaly index is only weakly related to the SST variations that control the response of the atmosphere in different climates, be they model climates, paleoclimates, or future climates. NOAA CPO FY17 COM 1 2 Nielsen-Gammon Statement of Work This sort of problem is not unique to ENSO. The Atlantic Multidecadal Oscillation is also defined in terms of temperature departures, in this case from a long-term linear trend. While the AMO is strongly correlated to Atlantic hurricane activity, it has been noted that a more physical correlation might be to the difference between tropical Atlantic temperatures and global tropical temperatures. So, in the Atlantic, the scientific community is already moving in the direction of a difference index as a replacement for the AMO index for some applications. (Third problem: relative vs. absolute indices) Motivated by the problems described above, and building on the work of Chiodi and Harrison, we propose to develop an ENSO index based on SST differences in the two tropical regions where convective activity (as measured by outgoing long wave radiation, or OLR) exhibits the largest ENSO-related variability. These regions are the NINO 3.4 region and the Maritime Continent (MC) region. The difference between these two regionally-averaged SSTs (3.4 – MC) reflects the extent to which SST variations act to keep tropical convection confined to the MC or allow it to spread eastward along the equator. Preliminary testing shows that such an index shares the advantage of an OLR-based index in being able to distinguish the dichotomy between weak and strong El Niño events and more precisely indicate extratropical teleconnections, but our SST-based index can also be applied to the pre-satellite era or to any ocean model simulations. It takes advantage of community familiarity with NINO 3.4 and related indices but does not require establishing a reference period from which anomalies should be calculated. The value of such an index is affected by the background modeled climate state or climate change, but in a good way, because it still quantifies the most physically relevant aspect of oceanic drivers of atmospheric response.
Improving the Drought Monitoring Capabilities of Land Surface Models by Integrating Bias-Corrected, Gridded Precipitation Estimates
Atmospheric Sciences; TAMU; https://hdl.handle.net/20.500.14641/453; DOC-National Oceanic and Atmospheric Administration
The “SPoRT-LIS” product is used by the NWS forecast offices in situational awareness (e.g., drought monitoring; White and Case 2015) as well as in local numerical modeling applications for enhanced initialization of land surface fields (Medlin et al. 2012). One of the limitations of the SPoRT-LIS accuracy is the input atmospheric forcing that drives the offline Noah land surface model simulation. The quality of the land surface model output is dependent on the input forcing fields, particularly the quantitative precipitation estimate (QPE). The current configuration of the SPoRT-LIS relies on long-term input forcing from the North American Land Data Assimilation System – Phase II product (NLDAS-2; Xia et al. 2012a; Xia et al. 2012b). The NLDAS-2 is the top choice for forcing due to its long duration (30+ years) and hourly output frequency. Due to real-time latency of 4 days in NLDAS-2, SPoRT-LIS bridges the gap for real-time output with supplemental hourly QPE from the Multi-Radar Multi-Sensor high-resolution radar/gauge blended product (Zhang et al. 2016) and global analyses from the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC). The limitations of NLDAS-2 forcing include real-time latency, coarse resolution grid (~15 km grid spacing), periodic quality issues arising from gauge quality control, and improper blending with the North American Regional Reanalysis (NARR). The primary limiting factors of implementing higher-resolution Multi-Radar/Multi-sensor (MRMS) QPE in a longer-term SPoRT-LIS are radar gaps and blending challenges on the peripheries of data coverage. Meanwhile, satellite QPE often introduces large biases due to inherent uncertainties in retrievals (e.g., Prat and Nelson 2015). SPoRT documented each of these QPE limitations and impacts on the soil moisture solution in a series of offline LIS simulations (Case et al. 2013). Problems with grid coverage and radar gaps are evident in the Stage IV and NMQ (currently MRMS) products, and the large over-estimation of satellite QPE was evident with the GOES-based algorithm. In order to provide an adequate spin-up of land surface model features, improved and bias-corrected QPE inputs are needed to preserve high spatial resolution features while offering seamless continuity in both space and time over a long duration simulation.
RAPID: Coordinated Rainfall Analysis of Hurricane Harvey
Atmospheric Sciences; TAMU; https://hdl.handle.net/20.500.14641/453; National Science Foundation
The landfall and slow movement of Hurricane Harvey created one of the most exceptional precipitation events on record in the United States. This rapid response award will result in the creation of a research-quality rainfall analysis product that can be used by hydrologists, meteorologists, and engineers to study flooding and extreme rainfall events. Traditional rainfall analysis products are limited by potential errors in the underlying data and differences in how ground-based measurements are mixed with weather radar estimates of rainfall. The researchers in this project will collect data about the rain gauges and test the various analysis techniques to provide a product that has high spatial and temporal resolution and quantifiable uncertainty estimates. The impact of the project will be to help improve simulations of urban flooding and to better understand extreme precipitation events. Local high school students will work on teams to collect data for the project, introducing them to scientific research activities. The research team will create a foundational set of rainfall data, metadata, and analyses for use in atmospheric, hydrologic and engineering studies of extreme precipitation events. One of the main project goals is to create a level of consistency and accuracy of the information that goes into the rainfall analyses. The research team will work with other stakeholders to establish metadata needs and standards and then collect the metadata from the network operators. Different rainfall analysis techniques and the sensitivity of the analyses to technique and parameter choices will also be assessed. One result of the project will be to provide analyses and uncertainty quantification to the research community. Other aspects of the research project will be to place Harvey's rainfall into historical context and to establish a dataset for comparison of rain gauge network performance.