Global warming-induced upper-ocean freshening and the intensification of super typhoons

Super typhoons (STYs), intense tropical cyclones of the western North Pacific, rank among the most destructive natural hazards globally. The violent winds of these storms induce deep mixing of the upper ocean, resulting in strong sea surface cooling and making STYs highly sensitive to ocean density stratification. Although a few studies examined the potential impacts of changes in ocean thermal structure on future tropical cyclones, they did not take into account changes in near-surface salinity. Here, using a combination of observations and coupled climate model simulations, we show that freshening of the upper ocean, caused by greater rainfall in places where typhoons form, tends to intensify STYs by reducing their ability to cool the upper ocean. We further demonstrate that the strengthening effect of this freshening over the period 1961–2008 is ∼53% stronger than the suppressive effect of temperature, whereas under twenty-first century projections, the positive effect of salinity is about half of the negative effect of ocean temperature changes.United States. Dept. of Energy. Regional & Global Climate Modeling Progra

Contribution of hurricane-induced sediment resuspension to coastal oxygen dynamics

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 8 (2018): 15740, doi:10.1038/s41598-018-33640-3.Hurricanes passing over the ocean can mix the water column down to great depths and resuspend massive volumes of sediments on the continental shelves. Consequently, organic carbon and reduced inorganic compounds associated with these sediments can be resuspended from anaerobic portions of the seabed and re-exposed to dissolved oxygen (DO) in the water column. This process can drive DO consumption as sediments become oxidized. Previous studies have investigated the effect of hurricanes on DO in different coastal regions of the world, highlighting the alleviation of hypoxic conditions by extreme winds, which drive vertical mixing and re-aeration of the water column. However, the effect of hurricane-induced resuspended sediments on DO has been neglected. Here, using a diverse suite of datasets for the northern Gulf of Mexico, we find that in the few days after a hurricane passage, decomposition of resuspended shelf sediments consumes up to a fifth of the DO added to the bottom of the water column during vertical mixing. Despite uncertainty in this value, we highlight the potential significance of this mechanism for DO dynamics. Overall, sediment resuspension likely occurs over all continental shelves affected by tropical cyclones, potentially impacting global cycles of marine DO and carbon.Support for J. Moriarty was provided by the USGS Mendenhall Program

Statistics of extreme events in coarse-scale climate simulations via machine learning correction operators trained on nudged datasets

This work presents a systematic framework for improving the predictions of statistical quantities for turbulent systems, with a focus on correcting climate simulations obtained by coarse-scale models. While high resolution simulations or reanalysis data are available, they cannot be directly used as training datasets to machine learn a correction for the coarse-scale climate model outputs, since chaotic divergence, inherent in the climate dynamics, makes datasets from different resolutions incompatible. To overcome this fundamental limitation we employ coarse-resolution model simulations nudged towards high quality climate realizations, here in the form of ERA5 reanalysis data. The nudging term is sufficiently small to not pollute the coarse-scale dynamics over short time scales, but also sufficiently large to keep the coarse-scale simulations close to the ERA5 trajectory over larger time scales. The result is a compatible pair of the ERA5 trajectory and the weakly nudged coarse-resolution E3SM output that is used as input training data to machine learn a correction operator. Once training is complete, we perform free-running coarse-scale E3SM simulations without nudging and use those as input to the machine-learned correction operator to obtain high-quality (corrected) outputs. The model is applied to atmospheric climate data with the purpose of predicting global and local statistics of various quantities of a time-period of a decade. Using datasets that are not employed for training, we demonstrate that the produced datasets from the ML-corrected coarse E3SM model have statistical properties that closely resemble the observations. Furthermore, the corrected coarse-scale E3SM output for the frequency of occurrence of extreme events, such as tropical cyclones and atmospheric rivers are presented. We present thorough comparisons and discuss limitations of the approach

Characteristics of Bay of Bengal monsoon depressions in the 21st Century

We show that 21st century increase in radiative forcing does not significantly impact the frequency of South Asian summer monsoon depressions (MDs) or their trajectories in the Coupled Model Intercomparison Project Phase 5 general circulation models (GCMs). A significant relationship exists between the climatological occurrences of MDs and the strength of the background upper (lower) tropospheric meridional (zonal) winds and tropospheric moisture in the core genesis region of MDs. Likewise, there is a strong relationship between the strength of the meridional tropospheric temperature gradient in the GCMs and the trajectories of MDs over land. While monsoon dynamics progressively weakens in the future, atmospheric moisture exhibits a strong increase, limiting the impact of changes in dynamics on the frequency of MDs. Moreover, the weakening of meridional tropospheric temperature gradient in the future is substantially weaker than its inherent underestimation in the GCMs. Our results also indicate that future increases in the extreme wet events are dominated by nondepression day occurrences, which may render the monsoon extremes less predictable in the future

A new global river network database for macroscale hydrologic modeling

Coarse-resolution (upscaled) river networks are critical inputs for runoff routing in macroscale hydrologic models. Recently, Wu et al. (2011) developed a hierarchical dominant river tracing (DRT) algorithm for automated extraction and spatial upscaling of river networks using fine-scale hydrography inputs. We applied the DRT algorithms using combined HydroSHEDS and HYDRO1k global fine-scale hydrography inputs and produced a new series of upscaled global river network data at multiple (1/16° to 2°) spatial resolutions. The new upscaled results are internally consistent and congruent with the baseline fine-scale inputs and should facilitate improved regional to global scale hydrologic simulations