Model Hindcast Research Articles

The second-generation real-time ecological environment prediction system for the Guangdong–Hong Kong–Marco Greater Bay Area: Model setup, validation, improvements, and online visualization

With the rapidly growing population and socioeconomic development of the Guangdong–Hong Kong–Marco Greater Bay Area of China, inputs of diverse contaminants have rapidly increased. This poses threats to the water quality of the Pearl River Estuary (PRE) and adjacent seas. To provide valuable information to assist the governors, stakeholders, and decision-makers in tracking changes in environmental conditions, daily nowcasts and two-day forecasts from the ecological prediction system, namely the Coupled Great Bay Ecological Environmental Prediction System (CGEEPS), has been setup. These forecast systems have been built on the Coupled Ocean–Atmosphere–Wave–Sediment Transport modelling system. This comprises an atmospheric Weather Research Forecasting module and an oceanic Regional Ocean Modelling System module. Daily real-time nowcasts and 2-day forecasts of temperature, salinity, NO2 + NO3, chlorophyll, and pH are continuously available. Visualizations of the forecasts are available on a local website (http://www.gbaycarbontest.xyz:8008/). This paper describes the setup of the environmental forecasting system, evaluates model hindcast simulations from 2014 to 2018, and investigates downscaling and two-way coupling with the regional atmospheric model. The results shown that though CGEEPS lacks accuracy in predicting the absolute value for biological and biogeochemical environmental variables. It is quite informative to predict the spatio-temporal variability of ecological environmental changes associated with extreme weather events. Our study will benefit of developing real-time marine biogeochemical and ecosystem forecast tool for oceanic regions heavily impact by extreme weathers.

Quantitative estimates of oil-seepage rates from satellite imagery with implications for oil generation and migration rates

A new approach to quantifying oil fluxes from natural seafloor seepage sites based on the dynamic characteristics of floating oil slicks is presented. It enables rapid analysis of interpreted satellite imagery and permits large datasets to be analyzed. The method uses the drift path of the oil on water surface, termed the “centerline” of the slick, along with average current and wind speeds to estimate the age of seepage slicks. The results of the CL (centerline) method compare favorably with the hindcast modeling approach which requires time-resolved analysis of wind and current for each slick. The CL method yields an average flux estimate within the range of the flux documented by on-bottom capture for a seafloor oil pollution source. A survey of published flux estimates using a variety of techniques reveals a wide range with on-bottom estimates (0.08–12 ml/s) generally lower than estimates from slicks (1.6–125 ml/s). Determination of fluxes from three seepage sites in the Gulf of Mexico (GC600, Bush Hill, and TYK) reveals a wide range in seepage rates from 2.4 to 52.9 ml/s and flux distributions. Comparison of the observed fluxes from seeps with potential fluxes from a source region can be used to constrain source rock parameters (e.g., thickness, richness, area) and to understand the cumulative oil supply represented by seeps over geologic time scales.

Interannual Variability of the Australian Summer Monsoon Sustained through Internal Processes: Wind–Evaporation Feedback, Dynamical Air–Sea Interaction, and Soil Moisture Memory

Abstract In northern Australia (NAUS), mean rainfall during the Australian summer monsoon (AUSM) season exhibits distinct interannual variability despite weak influence from tropical sea surface temperature (SST) variability. The present study investigates mechanisms for the strong and persistent rainfall anomalies throughout the AUSM season. When the AUSM is stronger than normal, the low-level monsoonal circulation intensifies in response to the stronger convective activity over NAUS. The intensified surface westerlies over the tropical southeastern Indian Ocean (SEIO) enhance oceanic evaporation locally and downstream moisture transport into NAUS. This wind–evaporation feedback is verified through a moist static energy budget analysis. For this feedback to work effectively, SST cooling due to the stronger AUSM should be weak enough not to suppress the oceanic evaporation in the tropical SEIO. Our mixed layer heat budget analysis based on an ocean model hindcast experiment reveals that anomalous downwelling in the subsurface SEIO, which is induced dynamically by the intensified monsoon westerlies, partially offsets the SST cooling. The land surface evaporation over the continental inland area is also enhanced significantly in the middle and later portions of the monsoon season associated with increased soil moisture, suggesting its memory effect for the persistence of rainfall anomalies. The AUSM variability can therefore be regarded as a self-sustaining internal variability in the atmosphere–ocean–land surface coupled system, rather than just an atmospheric internal variability. Significance Statement We aim to understand why summer monsoon rainfall in northern Australia varies markedly from one year to another even under the weak influence of large-scale sea surface temperature fluctuations, such as El Niño/La Niña. Our analyses based mainly on observational datasets reveal that wind-induced changes in oceanic evaporation south of Java significantly modulates the water vapor transport into northern Australia. We also find that ocean dynamics helps this wind–evaporation feedback process and continental soil moisture may act to prolong anomalous rainfall by its memory effect. This study shows the self-sustaining nature of the Australian summer monsoon variability under the atmosphere–ocean–land surface interactions, which deepens our understandings of the monsoon system.

Lagrangian Overturning Pathways in the Eastern Subpolar North Atlantic

Abstract The strength of the Atlantic meridional overturning circulation (AMOC) at subpolar latitudes is dominated by water mass transformation in the eastern subpolar North Atlantic Ocean (SPNA). However, the distribution of this overturning across the individual circulation pathways of both the Subpolar Gyre (SPG) and the Nordic seas overflows remains poorly understood. Here, we introduce a novel Lagrangian measure of the density-space overturning to quantify the principal pathways of the time-mean overturning circulation within an eddy-permitting ocean model hindcast. By tracing the trajectories of water parcels initialized from the northward inflows across the Overturning in the Subpolar North Atlantic Program (OSNAP) East section, we show that water mass transformation along the pathways of the eastern SPG accounts for 55% of the mean strength of the eastern subpolar AMOC. Water parcels following the dominant SPG pathway, sourced from the Subarctic Front, form upper North Atlantic Deep Water by circulating horizontally across sloping isopycnals in less than 2 years. A slower SPG route, entrained by overflow waters south of the Iceland–Faroes Ridge, is a crucial conduit for subtropical-origin water masses to penetrate the deep ocean on subdecadal time scales. On reproducing our findings using time-averaged velocity and hydrographic fields, we further show that the Nordic seas overflow pathways integrate multiple decades of water mass transformation before returning across the Greenland–Scotland Ridge. We propose that the strong disparity between the overturning time scales of the SPG (interannual) and the Nordic seas overflows (multidecadal) has important implications for the propagation of density anomalies within the eastern SPNA and hence the sources of AMOC variability.

The Response of the Subtropical Front to Changes in the Southern Hemisphere Westerly Winds—Evidence From Models and Observations

AbstractThe location of the Subtropical Front (STF), the boundary between Subtropical and Subantarctic Water in the Southern Ocean is proposed to be influenced by the strength and location of the Southern Hemisphere westerly winds. We use a hydrodynamic hindcast model and recent observations to test if changes in the westerly winds can cause meridional shifts in the STF over interannual to decadal timescales by modulating local Ekman transport. We find that increased, or northward, shifted westerly winds lead to an enhanced northward Ekman transport over large parts of the Southern Ocean, resulting in a northward shift in the STF. Conversely for weaker or southward shifted westerly winds where the STF migrates south. However, this relationship breaks down in regions with strong eddy variability and western boundary current systems. In these regions an increase in westerly winds lead to a southward shift in the STF. Observation data from 2004 to 2019 display a southward shift of STF associated with the positive Southern Annular Mode. However, the shift is smaller than the latitudinal shifts in the location of the zero wind stress curl and maximum westerly winds (−0.4° latitude/decade). This discrepancy is due to positive Ekman trends resulting from the intensification of the westerly winds, which oppose the southward migration. Changes in the Ekman transport and the overall southward shift of the STF have also resulted in an observed positive trend in chlorophyll‐a concentrations south of the STF, which could have ramifications for marine ecosystems and the biological pump in the Southern Ocean.

Impact of Yucatan Channel Subsurface Velocity Observations on the Gulf of Mexico State Estimates

Abstract The Gulf of Mexico (GoM) surface circulation variability is dominated by the Loop Current (LC) and the episodically released anticyclonic Loop Current eddies (LCEs). The Yucatan Current feeds the LC through the Yucatan Channel (YC), and its flow structure at the YC is hypothesized to affect the LC evolution critically. This study examines the impact of assimilating YC subsurface velocity observations from a tall mooring array across the YC on the GoM circulation. State estimates and forecasts of the LC circulation were produced using a regional implementation of the Massachusetts Institute of Technology general circulation model (MITgcm) and its adjoint-based four-dimensional variational (4DVAR) assimilation system. The estimates were constrained by combinations of the YC observations and satellite-derived sea surface height (SSH) and sea surface temperature (SST). The results show that assimilation of both moored and satellite data improves the model hindcasts and forecasts for all LC phases. Additionally, one realization of the state estimate that assimilates only moored data matches the LCE detachment timing with that of AVISO SSH. Observations from the moorings close to the Yucatan Peninsula significantly impact the LCE detachment. A finite-time Lyapunov exponent analysis reveals the differences among the assimilation experiments, such as eddylike structures intruding into the GoM through the YC, and its relation to the typical LC sudden growth. Finally, an adjoint sensitivity analysis is used to verify the dynamic link between the LC extension and the intrusion of eddylike structures into the GoM.

Enterococcus exceedances related to environmental variability at New Jersey ocean beaches.

Microbial pollution at ocean beaches is a global public health problem that can be exacerbated by excessive rainfall, particularly at beaches adjacent to urban areas. Rain is acknowledged as a predictive factor of Enterococcus levels at NJ beaches, but to date no study has explicitly examined the link. Here, five beaches (156 observations) in Monmouth County, NJ, with storm drain outflows present were sampled for Enterococcus and water quality during dry and wet periods. Hypotheses included (1) beaches differ in Enterococcus levels, (2) Enterococcus is present year-round, and (3) Enterococcus exceedances could be modeled based on environmental parameters. Beaches showed significantly different median Enterococcus levels, with site SEA2 (Neptune Blvd. in Deal, NJ) lower than others and site SEA4 (South Bath Ave. in Long Branch, NJ) higher than the other sites. Elevated Enterococcus levels were detected at water temperatures from 6.5 to 22.2°C. Multiple linear regression models identified rainfall (+), water temperature (+), and water level (-) as related to Enterococcus concentrations levels at these beaches. For the purpose of simulating the efficacy of different monitoring strategies, a hindcast model of Enterococcus abundance based on historic rainfall, water temperature, and water level data was produced. Results indicated that once-per-week sampling detected ~14% (e.g., 1/7) exceedance events, while sampling during summer alone detected ~ 50% of annual exceedance events. Models of Enterococcus exceedance based on readily available environmental time series have the potential to supplement and improve Enterococcus monitoring at NJ beaches.

A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy

This study describes the implementation and performances of a weather hindcast obtained by dynamically downscaling the ERA5 data across the period 1979–2019. The limited-area models used to perform the hindcast are BOLAM (with a grid spacing of 7 km over the Mediterranean domain) and MOLOCH (with a grid spacing of 2.5 km over Italy). BOLAM is used to provide initial and boundary conditions to the inner grid of the MOLOCH model, which is set in a convection-permitting configuration. The performances of such limited-area, high-resolution and long-term hindcast are evaluated comparing modelled precipitation data against two high-resolution gridded observational datasets. Any potential added-value of the BOLAM/MOLOCH hindcast is assessed with respect to ERA5-Land data, which are used as benchmark. Results demonstrate that the MOLOCH hindcast, compared to observations, provides a lower bias than ERA5-Land as regards both the mean annual rainfall (-1.3% vs 8.7%) and the 90th percentile of summer daily precipitation, although a wet bias is found in southern Italy (bias simeq 17.1%). Improvements are also gained in the simulation of the 90th percentile of hourly precipitations both in winter and, to a minor extent, in summer. The diurnal cycle of summer precipitations is found to be better reconstructed in the Alps than in the hilly areas of southern Italy. This study also presents the rainfall peaks obtained in the simulation of two well-known severe precipitation events that caused floods and damages in north-western Italy in 1994 and 2011. Finally, it is discussed how the demonstrated reliability of the BOLAM and MOLOCH models associated to the documented low computational cost, promote their use as a valuable tool for downscaling not only reanalyses but also climate projections.

Fast and Slow Subpolar Ocean Responses to the North Atlantic Oscillation: Thermal and Dynamical Changes

AbstractClimate model hindcasts are analyzed to reveal the impacts of the North Atlantic Oscillation (NAO) on the North Atlantic subpolar ocean, which exhibits variability on seasonal to decadal timescales. The ocean response to a single winter NAO event is separated into fast and slow responses. The fast response persists over winter–spring seasons, during which wind stress and heat flux anomalies associated with the NAO rapidly modify ocean temperatures via changes in Ekman transport and ocean‐atmosphere heat exchanges. The slow response persists for 3–4 years, during which overturning and gyre circulations redistribute opposing‐signed surface temperature anomalies created by the NAO. This redistribution modifies east‐west temperature contrasts altering the meridional heat transport associated with gyres and changing the strength of the overturning circulation. Hence, the fast and slow responses lead to opposing‐signed subpolar temperature anomalies in time from the competing effects of local forcing and horizontal heat convergence.

The tropical Atlantic as a negative feedback on ENSO

The influence of the tropical Atlantic on El Niño-Southern Oscillation (ENSO) is examined using dedicated climate model experiments with sea-surface temperature (SST) restoring. Partial SST restoring to climatology in the tropical Atlantic leads to slower decay of ENSO events and to a shift of the power spectrum to longer periods. Perfect model hindcast experiments with and without restoring tropical Atlantic SST to climatology indicate that both the northern tropical and equatorial Atlantic have a very small influence on ENSO development. During decaying ENSO events, on the other hand, northern tropical Atlantic SST anomalies strongly accelerate the decay. Key to the Atlantic influence on ENSO decay are Atlantic SST anomalies just north of the equator (~ 5°N). These lead to local convection anomalies that change the Walker circulation so as to accelerate ENSO decay. Importantly, anomalous events in either the northern tropical or equatorial Atlantic fail to develop in the hindcast ensemble mean, when tropical Pacific SSTs are restored to climatology. This indicates that anomalous tropical Atlantic events in boreal spring and summer are strongly dependent on preceding ENSO events in boreal winter. Thus, the role of the tropical Atlantic is to mediate a negative feedback of ENSO on itself. Despite this passive role of the tropical Atlantic in the Pacific-Atlantic interaction, accurate simulation of the Atlantic feedback should play some role in ENSO prediction. Further model experiments will be required to evaluate model dependence of these findings and to quantify the impact of the Atlantic on ENSO prediction skill.

Exceptional freshening and cooling in the eastern subpolar North Atlantic caused by reduced Labrador Sea surface heat loss

Abstract. Observations of the eastern subpolar North Atlantic in the 2010s show exceptional freshening and cooling of the upper ocean, peaking in 2016 with the lowest salinities recorded for 120 years. Published theories for the mechanisms driving the freshening include: reduced transport of saltier, warmer surface waters northwards from the subtropics associated with reduced meridional overturning; shifts in the pathways of fresher, cooler surface water from the Labrador Sea driven by changing patterns of wind stress; and the eastward expansion of the subpolar gyre. Using output from a high-resolution hindcast model simulation, we propose that the primary cause of the exceptional freshening and cooling is reduced surface heat loss in the Labrador Sea. Tracking virtual fluid particles in the model backwards from the eastern subpolar North Atlantic between 1990 and 2020 shows the major cause of the freshening and cooling to be an increased outflow of relatively fresh and cold surface waters from the Labrador Sea; with a minor contribution from reduced transport of warmer, saltier surface water northward from the subtropics. The cooling, but not the freshening, produced by these changing proportions of waters of subpolar and subtropical origin is mitigated by reduced along-track heat loss to the atmosphere in the North Atlantic Current. We analyse modelled boundary exchanges and water mass transformation in the Labrador Sea to show that since 2000, while inflows of lighter surface waters remain steady, the increasing output of these waters is due to reduced surface heat loss in the Labrador Sea beginning in the early 2000s. Tracking particles further upstream reveals that the primary source of the increased volume of lighter water transported out of the Labrador Sea is increased recirculation of water, and therefore longer residence times, in the upper 500–1000 m of the subpolar gyre.

Spatiotemporal continuous estimates of daily 1 km PM2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework

Abstract. High spatial resolution PM2.5 data covering a long time period are urgently needed to support population exposure assessment and refined air quality management. In this study, we provided complete-coverage PM2.5 predictions with a 1 km spatial resolution from 2000 to the present under the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/, last access: 3 October 2022) framework. To support high spatial resolution modeling, we collected PM2.5 measurements from both national and local monitoring stations. To correctly reflect the temporal variations in land cover characteristics that affected the local variations in PM2.5, we constructed continuous annual geoinformation datasets, including the road maps and ensemble gridded population maps, in China from 2000 to 2021. We also examined various model structures and predictor combinations to balance the computational cost and model performance. The final model fused 10 km TAP PM2.5 predictions from our previous work, 1 km satellite aerosol optical depth retrievals, and land use parameters with a random forest model. Our annual model had an out-of-bag R2 ranging between 0.80 and 0.84, and our hindcast model had a by-year cross-validation R2 of 0.76. This open-access, 1 km resolution PM2.5 data product, with complete coverage, successfully revealed the local-scale spatial variations in PM2.5 and could benefit environmental studies and policymaking.

A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale

AbstractUrban flooding from extreme precipitation and storm surge is a growing threat to cities, and detailed forecasts of urban inundation are needed for emergency response. We present a mechanistic framework to simulate flood inundation over metropolitan‐wide areas at fine resolution (3 m). A dual‐grid shallow‐water model is used to overcome computational bottlenecks, and an application to Hurricane Harvey focused on pluvial flooding provides a multi‐dimensional assessment of predictive skill. A hindcast model is shown to simulate peak stage across 41 stream gages with a mean absolute error (MAE) of 0.63 m, and hourly stage levels over a 5‐day period with a median MAE and Nash‐Sutcliffe Efficiency (NSE) of 0.74 m and 0.55, respectively. Peak flood level across 228 high water marks (HWMs) were captured with an MAE of 0.69 m. A forecast model forced by Quantitative Precipitation Forecast data is shown to be only marginally less accurate than the hindcast model. Peak stage is simulated with an MAE of 0.86 m, hourly stage is captured with a median MAE and NSE of 0.90 m and 0.41, respectively, and HWMs are captured with an MAE of 0.77 m. The forecast system also achieves hit rates of 90% and 73% predicting distress calls and FEMA damage claims, respectively, based on simulated flood depth. These results demonstrate the potential to operationally forecast pluvial flood inundation in the U.S. with the timeliness and accuracy needed for early warning, and we also highlight future research needs.

Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys

Due to recent technological advancements in the field of cloud-based satellite remote sensing, the barriers to global analysis ready dataset access and processing have lowered drastically increasing the opportunity for large-scale long-term satellite-derived shoreline monitoring solutions. However, validation studies employing synchronous and independent in-situ instantaneous shorelines at scale are limited. Thanks to a unique dataset of 47 synchronous UAV photogrammetric surveys in 15 coastal locations in Victoria, Australia, over 3 years, we validated Sentinel-2 instantaneous subpixel shorelines extracted using the most common water indices, CoastSat and a tidal-balanced convolutional neural network (Unet+++). We assessed the accuracy of the water level estimation using 9 wave run-up models and wave data sourced from a nearshore wave buoy network, a regional hindcast model and a global altimeter wave dataset. This paper also presents the first validation of Deep Learning-derived shorelines using synchronous independent in-situ data. We found the best overall performance and least threshold-sensitive shoreline extraction approach to be the WI index, whilst NDWI had the poorest performances despite being the most utilized across the literature. We found that the thresholdless Unet+++ solution coupled with an ensemble wave run-up model and the altimeter wave data is an adequate trade-off in terms of accuracy, adaptability and wave data access for global scale shoreline studies.Moreover, we highlighted the very high spatial variability of beach-scale shoreline extraction performances and found that generally, the best SDS extractor only performs best along 30–40% of a coastline. Our results suggest that future satellite-derived shoreline approaches should investigate the relationships between scene-dependent variables and shoreline accuracies in order to better understand the causes of beach-scale performance variations and increase the accuracy of satellite-derived shorelines.

Assessment of hot weather seasonal temperatures over India using Monsoon Mission Coupled Forecasting System hindcasts

AbstractThe present study evaluates the skill of seasonal forecasts of temperatures over India during April to June using the Monsoon Mission Coupled Forecasting System (MMCFS) model hindcasts, which are initialized with February initial conditions. Model hindcast data of 1981–2017 period have been used for the analysis. The India Meteorological Department (IMD) gridded temperature dataset has been used for model verifications. The MMCFS model captures the annual cycle of temperatures reasonably well, but with a higher mean and smaller variability compared to observations. The model hindcasts show a significant skill for seasonal forecasts of temperatures over most of northwest and central India. Empirical Orthogonal Function (EOF) analysis suggests that the model captures temporal and spatial characteristics of different modes of maximum temperatures but with less accuracy. The model teleconnections of maximum temperatures with Indian Ocean sea surface temperatures (SSTs) and El Niño–Southern Oscillation (ENSO) are weakly represented. The model is also found capable of predicting the spatial distribution of heat wave characteristics such as heat wave frequency (HWF) and heat wave duration (HWD) reasonably well. The present study suggests that the MMCFS Model can be used to generate a useful outlook of hot weather seasonal temperatures and heat waves over India.

Impact of environmental factors on heat-associated mortalities in an urban desert region

The troubling trend of rising heat-associated mortalities in an urban desert region (Maricopa County, AZ, USA) has motivated us to explore the extent to which environmental factors may contribute to increased heat-health risks. Summertime data from 2010 to 2019 were used to construct a suite of models for daily heat-associated mortalities. The best-performing full model included the following predictors, ordered from strongest to weakest influence: daily average air temperature, average of previous 5 days daily average air temperature, year, day of year, average of previous 5 days daily average dew point temperature, average of previous 5 days daily average PM2.5, and daily average PM10. This full model exhibited a 5.39% reduction in mean absolute error in daily heat-associated mortalities as compared to the best-performing model that included only air temperature as an environmental predictor. The extent to which issued and modeled excessive heat warnings (from both the temperature only and full models) corresponded with heat-associated mortalities was also examined. Model hindcasts for 2020 and 2021 showed that the models were able to capture the high number of heat-associated mortalities in 2020, but greatly undercounted the highest yet observed number of heat-associated mortalities in 2021. Results from this study lend insights into environmental factors corresponding to an increased number of heat-associated mortalities and can be used for informing strategies towards reducing heat-health risks. However, as the best-performing model was unable to fully capture the observed number of heat-associated mortalities, continued scrutiny of both environmental and non-environmental factors affecting these observations is needed.

A framework for feasibility-level validation of high-resolution wave hindcast models

The value of long-term wave hindcasts for investigating wave climates, wave energy resources, and extreme wave conditions has motivated research developing, calibrating and validating wave hindcast models. Past hindcast model validation studies examined the accuracy in modeling bulk wave parameters of overall sea states without considering the dependency of the model's skill within different sea states. In the present study, a framework for wave hindcast model validation is developed by examining the model accuracy for the most frequently occurring sea states, sea states contributing the most energy to total wave power, sea states associated with hurricane events, and those with the largest model error. Validations using bulk wave parameters and frequency-directional spectra at these key sea states and extreme wave conditions based on univariate and bivariate-contour methods provide insights to improve model accuracy, identifying the model's strong and weak points, and pathways for improvement, e.g., modeling wave-current interactions and adjusting wind data. This study adds to a growing body of research demonstrating that a carefully calibrated and verified spectral wave hindcast model can be used to estimate key wave energy parameters over a wide range of wave energy climates, as well as their spatial, temporal, frequency, directional, and probabilistic distributions.

Climate change and Arenicola marina: Heat waves and the southern limit of an ecosystem engineer

In asking how ecosystems will change with the forces of climate change, one approach is to identify the biotic forcing functions and focus on the ecologically dominant species, because as those species are affected, results will cascade through the system altering components such as biological diversity. Given the status of A. marina as an important ecosystem driver, it is of interest to ask how increasing temperatures will impact its distribution. The literature strongly suggests that the period during which it is most susceptible is late summer when the metabolic load of gametes is maximal and sediment temperatures at burrow depth during low tide are likely to exceed 25 °C. We used the probability of occurrence of fatal heat waves that penetrate to animal burrow depth as a primary factor in our modeling of past, present and future climates and the distribution of A. marina. Modeled sediment temperatures were validated against in situ temperature logger data and then used to develop the relationship between August SST and the occurrence of fatal heat waves to enable use of SST data directly. Modeled thermal gradients corresponded well to high resolution remotely sensed temperatures along the French and Iberian coasts and to distribution shifts in A. marina, known both from the literature and field surveys done by the authors in 2005–2014. Model hindcasts suggest that published occurrences of A. marina within the Mediterranean after 1860 except within the Gulf of Lions are unlikely. Given the known sensitivity of A. marina to anaerobiosis during heat waves, particularly when ripe, the forecasts based on CMIP6 25 km scale ocean models all predict the complete disappearance of A. marina from the Mediterranean and restriction to the upwelling zone on the western margin of Iberia and to the Atlantic coast of Europe north of 45°N with the suggestion of more limited populations in the English Channel and perhaps into the Wadden Sea. These biogeographic shifts will likely cause fundamental changes in ecosystem function along the coasts of Iberia, France, UK, and perhaps into Belgium and the Netherlands.

Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness

This paper introduces a method for estimating a series of sea state parameters from satellite-borne synthetic aperture radar (SAR). The method was realized in a near real time (NRT) application which allows for the processing of data from different satellites and modes. The algorithm estimates the total significant wave height Hs, dominant and secondary swell and windsea wave heights, first, and second moment wave periods, the mean wave period and the period of wind sea. The algorithm was applied to the Sentinel-1 (S1) C-band Interferometric Wide Swath Mode (IW), Extra Wide (EW) and Wave Mode (WV) Level-1 (L1) products and also extended to X-band TerraSAR-X (TS-X) StripMap (SM) products. The scenes are processed in raster and result in continuous sea state fields, with the exception of S1 WV, where averaged values for sea state parameters for along-orbit imagettes of 20 km × 20 km are presented.The developed empirical algorithm consists of two parts: a first CWAVE_EX (extended CWAVE) part, based on a linear regression approach, and a subsequent machine learning part using the support vector machine (SVM) technique. A series of new data preparation steps (i.e. filtering, denoising) and new features estimated from SAR images are also introduced. The algorithm was tuned and validated using two independent global wave model hindcasts, WaveWatch-3 and MFWAM as well as National Data Buoy Center (NDBC) measurements. The achieved root mean squared errors (RMSE) for CWAVE_EX for the total Hs are 0.60 m for low-resolution modes S1 IW (10 m pixel) and EW (40 m pixel) and 0.35 m for S1 WV and TS-X SM (pixel spacing ca.1–4 m) in comparison to model predictions. The RMSEs of the retrieved wave periods are in the range of 0.45–0.90 s for all of the satellites and models considered. Similarly, the dominant and secondary swell, and wind sea wave height RMSEs are in the range of 0.35–0.80 m. The SVM postprocessing improves the accuracy of the initial results of CWAVE_EX for Hs and reaches an RMSE of 0.25 m for S1 WV. Comparisons to 64 NDBC buoys, collocated at distances shorter than 50 km to S1 WV imagettes worldwide, result in an RMSE of 0.41 m. All results and the methods presented are novel in terms of the accuracy achieved, combining the classical approach with machine learning techniques, and performing an automatic NRT processing of multiparametric sea state fields from L1 data with automatic switching for different satellites and modes.The complete archive of S1 WV L1 Single Look Complex products from December 2014 until February 2021 was processed to create a sea state parameter database and validated using model hindcast and buoy measurements. The derived parameters are available to the public within the scope of the European Space Agency's Climate Change Initiative.

Impact of reduced ENSO variability and amplitude on ISMR prediction in the long‐lead forecasts of monsoon mission CFS

AbstractDespite the reduction in interannual variability and amplitude of El Nino Southern Oscillation (ENSO) after 2000, its influence on Indian summer monsoon rainfall (ISMR) is stronger than in the previous decades. Meanwhile, our analysis with high‐resolution Monsoon Mission Climate Forecast system (MMCFS, ~38 km horizontal resolution) indicates a sharp decrease in ISMR skill and ENSO–ISMR teleconnection since 2000 at long lead (3 months, Feb IC) hindcasts. At the same time, ISMR skill is intensified for a short lead (1 month, Apr IC). The persistence skill of ENSO decreased after 2000, but the decrease is more severe for January–March initial months. So the ENSO skill will be lower for models initialized during these months compared with other months. Also, the Feb IC hindcast has a similar ENSO teleconnection pattern during both periods and hence it failed to capture the modified influence of ENSO after 2000, such as the absence of dipole anomalies in the Indian Ocean and stronger subsidence over India. Thus, the model hindcasts that depend heavily on ENSO teleconnection for ISMR simulation have reduced skill in recent years. The role of other predictors along with ENSO for Apr IC hindcasts is hence evident in the current period. It is shown that after 2000, tropical Atlantic SST anomalies contributed significantly to ISMR interannual variability in observations. In these, Atlantic Meriodnal Mode (AMM) influences ISMR through off‐ equatorial circulation in absence of ENSO during the second period. Atlantic zonal mode has a weaker influence in absence of ENSO during both periods. Atlantic SST influence on ISMR is captured by April IC hindcasts during period 2, while Feb IC has the opposite relationship. Thus, the presence of another predictor in the form of tropical Atlantic SST along with strong ENSO–ISMR teleconnection makes the short lead hindcast of ISMR more skilful in the recent period for MMCFS.