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Rising seas : flooding, climate change and our new world
\"The Earth's oceans are on the rise. Since 1900, global sea levels have risen steadily each year to a global average of about 8 inches (20cm) today, and they're still rising. By 2100, the sea could climb as much as 14 feet (4.3m) to 32 feet (9.75m). Rising Seas: Flooding, Climate Change and Our New World gives youth an eye-popping view of what the Earth might look like under the rising and falling water levels of climate change. Photographs juxtapose the present-day with that same area's projected future. The shocking images will help them understand the urgency for action. Key issues in today's news will be better understood, such as the 2015 Paris Protocol in which the world agreed to limit temperature increases to 2 degrees Celsius (ideally 1.5 degree). This new edition features three new locations, updated information and new features about climate anxiety and reasons to hope for change.\"-- Provided by publisher
Book
Statistical Downscaling of Seasonal Forecasts of Sea Level Anomalies for U.S. Coasts
Increasing coastal inundation risk in a warming climate will require accurate and reliable seasonal forecasts of sea level anomalies at fine spatial scales. In this study, we explore statistical downscaling of monthly hindcasts from six current seasonal prediction systems to provide a high‐resolution prediction of sea level anomalies along the North American coast, including at several tide gauge stations. This involves applying a seasonally invariant downscaling operator, constructing by linearly regressing high‐resolution (1/12°) ocean reanalysis data against its coarse‐grained (1°) counterpart, to each hindcast ensemble member for the period 1982–2011. The resulting high‐resolution coastal hindcasts have significantly more deterministic skill than the original hindcasts interpolated onto the high‐resolution grid. Most of this improvement occurs during summer and fall, without impacting the seasonality of skill noted in previous studies. Analysis of the downscaling operator reveals that it boosts skill by amplifying the most predictable patterns while damping the less predictable patterns. Plain Language Summary Currently, the large computer models that form the basis of seasonal climate prediction systems produce coastal sea level forecasts spaced about 100 km apart. This is too coarse to meet the needs of U.S. coastal ocean management and services, which are becoming increasingly important as sea levels rise in a warming climate. In this study, we explored a method to provide such information on much smaller spatial scales, which better correspond to local coastal sea level measurements by tide gauges. We developed an efficient way to generate monthly sea level predictions on distances as small as 10 km apart, by applying the observed statistical relationship between sea level variations on scales of 100–1,000 km and finer‐scale coastal ocean observations to the original coarser model predictions. By testing our approach on past forecasts (“hindcasts”) from existing climate forecast systems, we found that we could improve forecasts for different local regions along both the U.S. West and East Coasts. Key Points Sea level prediction from relatively coarse operational forecasts can be enhanced to finer coastal scales using statistical downscaling Downscaling can be determined by multivariate linear regression trained from high‐resolution reanalysis and its coarse‐grained counterpart This downscaling method significantly improves skill compared to bilinearly interpolated hindcasts at several U.S. tide gauge locations
Journal Article
A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting
The accumulated remote sensing data of altimeters and scatterometers have provided new opportunities for ocean state forecasting and have improved our knowledge of ocean–atmosphere exchanges. Studies on multivariate, multistep, spatiotemporal sequence forecasts of sea level anomalies (SLA) for different modalities, however, remain problematic. In this paper, we present a novel hybrid and multivariate deep neural network, named HMnet3, which can be used for SLA forecasting in the South China Sea (SCS). First, a spatiotemporal sequence forecasting network is trained by an improved convolutional long short-term memory (ConvLSTM) network using a channelwise attention mechanism and multivariate data from 1993 to 2015. Then a time series forecasting network is trained by an improved long short-term memory (LSTM) network, which is realized by ensemble empirical mode decomposition (EEMD). Finally, the two networks are combined by a successive correction method to produce SLA forecasts for lead times of up to 15 days, with a special focus on the open sea and coastal regions of the SCS. During the testing period of 2016–18, the performance of HMnet3 with sea surface temperature anomaly (SSTA), wind speed anomaly (SPDA), and SLA data is much better than those of state-of-the-art dynamic and statistical (ConvLSTM, persistence, and climatology) forecast models. Stricter testbeds for trial simulation experiments with real-time datasets are investigated, where the eddy classification metrics of HMnet3 are favorable for all properties, especially for those of small-scale eddies.
Journal Article
Assessing subseasonal forecast skill for use in predicting US coastal inundation risk
Developing predictions of coastal flooding risk on subseasonal timescales (2–6 weeks in advance) is an emerging priority for the National Oceanic and Atmospheric Administration (NOAA). In this study, we assess the ability of two current operational forecast systems, the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) and the Centre National de Recherches Météorologiques climate model (CNRM), to make subseasonal ensemble predictions of the non-tidal residual component of coastal water levels at United States coastal gauge stations for the period 2000–2019. These models were chosen because they assimilate satellite altimetry at forecast initialization and attempt to predict the mean sea level, including a global mean component whose absence in other forecast systems complicates assessment of tide gauge reforecast skill. Both forecast systems have skill that exceeds damped persistence for forecast leads through 2–3 weeks, with IFS skill exceeding damped persistence for leads up to 6 weeks. Post-processing forecasts to include the inverse barometer effect, derived from mean sea level pressure forecasts, improves skill for relatively short forecast leads (1–3 weeks). Accounting for vertical land motion of each gauge primarily improves skill for longer leads (3–6 weeks), especially for the Alaskan and Gulf coasts; sea-level trends contribute to reforecast skill for both model and persistence forecasts, primarily for the East and Gulf coasts. Overall, we find that current forecast systems have sufficiently high levels of deterministic and probabilistic skill to be used in support of operational coastal flood guidance on subseasonal timescales.
Journal Article
Contrasted influence of climate modes teleconnections to the interannual variability of coastal sea level components–implications for statistical forecasts
Sea level variations at the coast can have drastic environmental and socio-economic impacts in particular in the context of an ever-increasing coastal population and anthropogenic climate change. Regional to global climate variability influences all these factors and exerts a strong control on the coastal sea level over a wide range of time scales. Here, we focus on understanding interannual changes which is paramount to improve interannual forecasting systems as well as to constrain and reduce uncertainties on the secular trend in global mean sea level. We consider the coastal total water level (TWL) as the compound effect of three main components: the wave setup, mean regional sea level anomaly (i.e., steric and ocean circulation influences) and atmospheric surge (i.e., influence of local wind and surface atmospheric pressure). To understand their variability at a global scale, we focus on the effect of four climate modes that affect the major oceanic basins: the El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO) and the Southern Annual Mode (SAM). The contrasted regional influence of these different climate modes on the interannual variations of TWL components are quantified. Results suggest that even if the regional mean sea level is overall the main contributor to the interannual variations of TWL variations at the coast and mostly related to ENSO, the contributions from wave setup and atmospheric surge are not negligible in particular at high latitudes and mostly related to the NAO in the Northern Atlantic and to the SAM in the Southern Hemisphere. Such influences from the NAO and SAM can be seen far away from their extratropical regions of action due to their atmospheric forcing of ocean waves that can significantly propagate their imprint towards tropical areas. Implications for interannual to decadal forecasts of the coastal TWL and related hazards are discussed in the light of regression statistical models and the climate modes own predictability.
Journal Article
Improving sub-seasonal forecast skill of meteorological drought: a weather pattern approach
Dynamical model skill in forecasting extratropical precipitation is limited beyond the medium-range (around 15 d), but such models are often more skilful at predicting atmospheric variables. We explore the potential benefits of using weather pattern (WP) predictions as an intermediary step in forecasting UK precipitation and meteorological drought on sub-seasonal timescales. Mean sea-level pressure forecasts from the European Centre for Medium-Range Weather Forecasts ensemble prediction system (ECMWF-EPS) are post-processed into probabilistic WP predictions. Then we derive precipitation estimates and dichotomous drought event probabilities by sampling from the conditional distributions of precipitation given the WPs. We compare this model to the direct precipitation and drought forecasts from the ECMWF-EPS and to a baseline Markov chain WP method. A perfect-prognosis model is also tested to illustrate the potential of WPs in forecasting. Using a range of skill diagnostics, we find that the Markov model is the least skilful, while the dynamical WP model and direct precipitation forecasts have similar accuracy independent of lead time and season. However, drought forecasts are more reliable for the dynamical WP model. Forecast skill scores are generally modest (rarely above 0.4), although those for the perfect-prognosis model highlight the potential predictability of precipitation and drought using WPs, with certain situations yielding skill scores of almost 0.8 and drought event hit and false alarm rates of 70 % and 30 %, respectively.
Journal Article
Multimodel Ensemble Sea Level Forecasts for Tropical Pacific Islands
Sea level anomaly extremes impact tropical Pacific Ocean islands, often with too little warning to mitigate risks. With El Niño, such as the strong 2015/16 event, comes weaker trade winds and mean sea level drops exceeding 30 cm in the western Pacific that expose shallow-water ecosystems at low tides. Nearly opposite climate conditions accompany La Niña events, which cause sea level high stands (10–20 cm) and result in more frequent tide- and storm-related inundations that threaten coastlines. In the past, these effects have been exacerbated by decadal sea level variability, as well as continuing global sea level rise. Climate models, which are increasingly better able to simulate past and future evolutions of phenomena responsible for these extremes (i.e., El Niño–Southern Oscillation, Pacific decadal oscillation, and greenhouse warming), are also able to describe, or even directly simulate, associated sea level fluctuations. By compiling monthly sea level anomaly predictions from multiple statistical and dynamical (coupled ocean–atmosphere) models, which are typically skillful out to at least six months in the tropical Pacific, improved future outlooks are achieved. From this multimodel ensemble comes forecasts that are less prone to individual model errors and also uncertainty measurements achieved by comparing retrospective forecasts with the observed sea level. This framework delivers online a new real-time forecasting product of monthly mean sea level anomalies and will provide to the Pacific island community information that can be used to reduce impacts associated with sea level extremes.
Journal Article
Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast
We have designed a machine learning method to predict the occurrence of daily extreme sea level at the Baltic Sea coast with lead times of a few days. The method is based on a random forest classifier. It uses spatially resolved fields of daily sea level pressure, surface wind, precipitation, and the pre-filling state of the Baltic Sea as predictors for daily sea level above the 95 % quantile at each of seven tide gauge stations representative of the Baltic coast. The method is purely data-driven and is trained with sea level data from the Global Extreme Sea Level Analysis (GESLA) dataset and from the meteorological reanalysis ERA5 of the European Centre for Medium-Range Weather Forecasts (ECMWF). Sea level extremes at lead times of up to 3 d are satisfactorily predicted by the method, and the relevant predictor and predictor regions are identified. The sensitivity, measured as the proportion of correctly predicted extremes, is, depending on the stations, on the order of 70 %. The precision of the model is typically around 25 % and, for some instances, higher. For lead times longer than 3 d, the predictive skill degrades; for 7 d, it is comparable to a random skill. The sensitivity of our model is higher than the one derived from a storm surge reanalysis with dynamical models that use available information of the predictors without any time lag, as done by Muis et al. (2016), but its precision is considerably lower. The importance of each predictor depends on the location of the tide gauge. Usually, the most relevant predictors are sea level pressure, surface wind, and pre-filling. Extreme sea levels at the meridionally oriented coastlines of the Baltic Sea are better predicted by meridional winds and surface pressure. In contrast, for stations located at zonally oriented coastlines, the most relevant predictors are surface pressure and the zonal wind component. Precipitation did not display consistent patterns or a high relevance predictor for most of the stations analysed. The random forest classifier is not required to have considerable complexity, and the computing time to issue predictions is typically a few minutes on a personal laptop. The method can, therefore, be used as a pre-warning system to trigger the application of more sophisticated algorithms that estimate the height of the ensuing extreme sea level or as a warning to run larger ensembles with physically based numerical models.
Journal Article
Comparative study of multivariate hybrid neural networks for global sea level prediction through 2050
The ongoing rise in global sea levels poses significant risks to coastal regions such as storms surges, floodings and necessitates accurate predictive models to inform the relevant government organizations that are responsible of mitigation strategies. This study leverages advanced hybrid deep learning techniques to forecast global sea level changes up to the year 2050. Utilizing a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, our model integrates historical global sea level data from climate.gov and global air temperature projections from the CMIP6 (Coupled Model Intercomparison Project Phase 6) model. Performance evaluation, based on metrics such as Nash-Sutcliffe Efficiency, Mean Squared Error (MSE), and the Diebold-Mariano Test, demonstrates the superior accuracy of the hybrid models over traditional deep learning models. Results show that the hybrid LSTM-CNN model outperforms the standalone models, achieving an MSE of 0.4644 mm and an NSE of 0.9994, compared to the LSTM model’s MSE of 2.4450 mm and NSE of 0.9970. These findings underscore the potential of deep learning methodologies in enhancing the precision of long-term sea level predictions, providing valuable insights for policymakers and researchers in climate science.
Journal Article
Impact of CYGNSS-Derived Winds on Tropical Cyclone Forecasts in a Global and Regional Model
An observing system experiment was conducted to assess the impact of wind products derived from the Cyclone Global Navigation Satellite System (CYGNSS) on tropical cyclone track, maximum 10-m wind speed V max , and minimum sea level pressure forecasts. The experiment used a global data assimilation and forecast system, and the impact of both CYGNSS-derived scalar and vector wind retrievals was investigated. The CYGNSS-derived vector wind products were generated by optimally combining the scalar winds and a gridded a priori vector field. Additional tests investigated the impact of CYGNSS data on a regional model through the impact of lateral boundary and initial conditions from the global model during the developmental phase of Hurricane Michael (2018). In the global model, statistically significant track forecast improvements of 20–40 km were found in the first 60 h. The V max forecasts showed some significant degradations of ~2 kt at a few lead times, especially in the first 24 h. At most lead times, impacts were not statistically significant. Degradations in V max for Hurricane Michael in the global model were largely attributable to a failure of the CYGNSS-derived scalar wind test to produce rapid intensification in the forecast initialized at 0000 UTC 7 October. The storm in this test was notably less organized and symmetrical than in the control and CYGNSS-derived vector wind test. The regional model used initial and lateral boundary conditions from the global control and CYGNSS scalar wind tests. The regional forecasts showed large improvements in track, V max , and minimum sea level pressure.
Journal Article