The physics of blue crab larval recruitment in Delaware Bay: A model study

Recent studies have shown that the tidal-, wind- and buoyancy-driven surface currents govern the transport of blue crab (Callinectes sapidus) larvae within the coastal ocean and estuaries. Here, we develop a model of larval transport within Delaware Bay and the adjoining coastal ocean using a particle advection scheme coupled to a previously validated physical circulation model which includes realistic tidal forcing, bottom bathymetry, wind stress and river discharge. The coupled model is then used to quantify the effects of several mechanisms on larval transport and recruitment in this region and hindcast actual larval settlement for a four-year period.The model is run for the years 1989–1992 and compared with observations of larval settlement collected in the Broadkill River, a small tributary to Delaware Bay. It is able to reproduce all of the major observed recruitment events in 1990–1992, suggesting that larval recruitment is primarily driven by the physical mechanisms included in the model. Analysis of the modeled particle trajectories and the settlement data reveals that wind stress is the dominant mechanism in the determination of the timing of the settlement events, while horizontal diffusion and mortality determine the magnitude of the events. The model fails to agree with observations in 1989, indicating that small-scale physical events as well as larval behavior not reproduced in the numerical model can be important in larval settlement

CYGNSS data map flood inundation during the 2017 Atlantic hurricane season.

The 2017 Atlantic Hurricane Season was one of the most active and destructive on record, leading to significant flooding in many parts of the United States and the Caribbean. During flooding events such as these, there is an urgent need to quickly map in detail which areas have been severely affected, yet current satellite missions are not capable of sampling the global land surface at high enough spatio-temporal scales for flooding applications. Here, we demonstrate a novel approach to high-resolution flood mapping by repurposing data from the new NASA mission, CYGNSS. The CYGNSS multi-satellite constellation was designed for frequent temporal sampling of the ocean surface in the tropics. We demonstrate that CYGNSS data provide clear signals of surface saturation and inundation extent over land at higher spatio-temporal resolution than radiometers like SMAP. Using a simple thresholding technique, we are able to estimate that approximately 32,580 km2 of land area in Texas flooded during Hurricane Harvey, and approximately 7210 km2 of land area flooded in Cuba during Hurricane Irma, or about 7% of Cuba's total area.</p

The Sensitivity of US Wildfire Occurrence to Pre-Season Soil Moisture Conditions Across Ecosystems

It is well accepted that drought and low moisture conditions are linked with increased wildfire occurrence. However, quantifying the sensitivity of wildfire to surface moisture state has been challenging due to a lack of soil moisture observations at an appropriate spatial scale. Here we apply model simulations of surface soil moisture that numerically assimilate observations from NASAs Gravity Recovery and Climate Experiment (GRACE) mission, combined in a predictive algorithm with the US Forest Services Fire-Occurrence Database. We estimate a relationship between historic surface moisture and wildfire occurrence to produce annual probable wildfire occurrence and burned area at 0.25-degree resolution for the contiguous United States by land-cover classification. Cross-validation indicates increased frequency of smaller fires when the months preceding fire season are wet, while larger fires are more frequent when soils are dry. This demonstrates that assimilated GRACE data holds information that could aid national-scale fire potential assessments for early decision-support

The Sensitivity of US Wildfire Occurrence to Pre-Season Soil Moisture Conditions Across Ecosystems

It is generally accepted that year-to-year variability in moisture conditions and drought are linked with increased wildfire occurrence. However, quantifying the sensitivity of wildfire to surface moisture state at seasonal lead-times has been challenging due to the absence of a long soil moisture record with the appropriate coverage and spatial resolution for continental-scale analysis. Here we apply model simulations of surface soil moisture that numerically assimilate observations from NASA's Gravity Recovery and Climate Experiment (GRACE) mission with the US Forest Service"TM"s historical Fire-Occurrence Database over the contiguous United States. We quantify the relationships between pre-fire-season soil moisture and subsequent-year wildfire occurrence by land-cover type and produce annual probable wildfire occurrence and burned area maps at 0.25-degree resolution. Cross-validated results generally indicate a higher occurrence of smaller fires when months preceding fire season are wet, while larger fires are more frequent when soils are dry. This result is consistent with the concept of increased fuel accumulation under wet conditions in the pre-season. These results demonstrate the fundamental strength of the relationship between soil moisture and fire activity at long lead-times and are indicative of that relationship's utility for the future development of national-scale predictive capability

Asymmetric Response of Land Storage to ENSO Phase and Duration

Emergence of global mean sea level (GMSL) from a 'hiatus' following a persistent La Ni&ntilde;a highlights the need to understand the causes of interannual variability in GMSL. Several studies link interannual variability in GMSL to anomalous transport of water mass between land and ocean-and subsequent changes in water storage in these reservoirs-primarily driven by El Ni&ntilde;o/Southern Oscillation (ENSO). Despite this, asymmetries in teleconnections between ENSO mode and land water storage have received less attention. We use historical simulations of natural climate variability to characterize asymmetries in the hydrological response to ENSO based on phase and duration. Findings indicate pronounced phase-specific and duration-specific asymmetries covering up to 93 and 50 million km2 land area, respectively. The asymmetries are seasonally dependent, and based on the mean regional climate are capable of influencing inherently bounded storage by pushing the storage-precipitation relationship towards nonlinearity. The nonlinearities are more pronounced in dry regions in the dry season, wet regions in the wet season, and during Year 2 of persistent ENSO events, limiting the magnitude of associated anomalies under persistent ENSO influence. The findings have implications for a range of stakeholders, including sea level researchers and water managers.</p

Origin of interannual variability in global mean sea level

Author Posting. © National Academy of Sciences, 2020. This article is posted here by permission of National Academy of Sciences for personal use, not for redistribution. The definitive version was published in Proceedings of the National Academy of Sciences of the United States of America 117(25), (2020): 13983-13990, doi: 10.1073/pnas.1922190117.The two dominant drivers of the global mean sea level (GMSL) variability at interannual timescales are steric changes due to changes in ocean heat content and barystatic changes due to the exchange of water mass between land and ocean. With Gravity Recovery and Climate Experiment (GRACE) satellites and Argo profiling floats, it has been possible to measure the relative steric and barystatic contributions to GMSL since 2004. While efforts to “close the GMSL budget” with satellite altimetry and other observing systems have been largely successful with regards to trends, the short time period covered by these records prohibits a full understanding of the drivers of interannual to decadal variability in GMSL. One particular area of focus is the link between variations in the El Niño−Southern Oscillation (ENSO) and GMSL. Recent literature disagrees on the relative importance of steric and barystatic contributions to interannual to decadal variability in GMSL. Here, we use a multivariate data analysis technique to estimate variability in barystatic and steric contributions to GMSL back to 1982. These independent estimates explain most of the observed interannual variability in satellite altimeter-measured GMSL. Both processes, which are highly correlated with ENSO variations, contribute about equally to observed interannual GMSL variability. A theoretical scaling analysis corroborates the observational results. The improved understanding of the origins of interannual variability in GMSL has important implications for our understanding of long-term trends in sea level, the hydrological cycle, and the planet’s radiation imbalance.The research was carried out at JPL, California Institute of Technology, under a contract with NASA. This study was funded by NASA Grants NNX17AH35G (Ocean Surface Topography Science Team), 80NSSC17K0564, and 80NSSC17K0565 (NASA Sea Level Change Team). The efforts of J.T.F. in this work were also supported by NSF Award AGS-1419571, and by the Regional and Global Model Analysis component of the Earth and Environmental System Modeling Program of the US Department of Energy's Office of Biological & Environmental Research via National Science Foundation Grant IA 1844590. C.G.P. was supported by the J. Lamar Worzel Assistant Scientist Fund and the Penzance Endowed Fund in Support of Assistant Scientists at the Woods Hole Oceanographic Institution.2020-12-0

Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression

Modeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low-quality well data which is sparsely and noisily sampled across time and space. A novel machine learning method is proposed for modeling groundwater levels by learning from a 3D lithological texture model of the CV aquifer. The proposed formulation performs multivariate regression by combining Gaussian processes (GP) and deep neural networks (DNN). Proposed hierarchical modeling approach constitutes training the DNN to learn a lithologically informed latent space where non-parametric regression with GP is performed. The methodology is applied for modeling groundwater levels across the CV during 2015 - 2020. We demonstrate the efficacy of GP-DNN regression for modeling non-stationary features in the well data with fast and reliable uncertainty quantification. Our results indicate that the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.Comment: Submitted to Water Resources Researc

Influence of nonseasonal river discharge on sea surface salinity and height

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chandanpurkar, H. A., Lee, T., Wang, X., Zhang, H., Fournier, S., Fenty, I., Fukumori, I., Menemenlis, D., Piecuch, C. G., Reager, J. T., Wang, O., & Worden, J. Influence of nonseasonal river discharge on sea surface salinity and height. Journal of Advances in Modeling Earth Systems, 14(2), (2022): e2021MS002715, https://doi.org/10.1029/2021MS002715.River discharge influences ocean dynamics and biogeochemistry. Due to the lack of a systematic, up-to-date global measurement network for river discharge, global ocean models typically use seasonal discharge climatology as forcing. This compromises the simulated nonseasonal variation (the deviation from seasonal climatology) of the ocean near river plumes and undermines their usefulness for interdisciplinary research. Recently, a reanalysis-based daily varying global discharge data set was developed, providing the first opportunity to quantify nonseasonal discharge effects on global ocean models. Here we use this data set to force a global ocean model for the 1992–2017 period. We contrast this experiment with another experiment (with identical atmospheric forcings) forced by seasonal climatology from the same discharge data set to isolate nonseasonal discharge effects, focusing on sea surface salinity (SSS) and sea surface height (SSH). Near major river mouths, nonseasonal discharge causes standard deviations in SSS (SSH) of 1.3–3 practical salinity unit (1–2.7 cm). The inclusion of nonseasonal discharge results in notable improvement of model SSS against satellite SSS near most of the tropical-to-midlatitude river mouths and minor improvement of model SSH against satellite or in-situ SSH near some of the river mouths. SSH changes associated with nonseasonal discharge can be explained by salinity effects on halosteric height and estimated accurately through the associated SSS changes. A recent theory predicting river discharge impact on SSH is found to perform reasonably well overall but underestimates the impact on SSH around the global ocean and has limited skill when applied to rivers near the equator and in the Arctic Ocean.This research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) with support from the Physical Oceanography (PO) and Modeling, Analysis, and Prediction (MAP) Programs. High-end computing resources for the numerical simulation were provided by the NASA Advanced Supercomputing Division at the Ames Research Center