Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model

Marine biogeochemical (BGC) models are highly uncertain in their parameterization. The value of the BGC parameters are poorly known and lead to large uncertainties in the model outputs. This study focuses on the uncertainty quantification of model fields and parameters within a one-dimensional (1-D) ocean BGC model applying ensemble data assimilation. We applied an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) into a 1-D vertical configuration of the BGC model Regulated Ecosystem Model 2 (REcoM2) at two BGC time-series stations: the Bermuda Atlantic Time-series Study (BATS) and the Dynamique des Flux Atmosphériques en Méditerranée (DYFAMED). We assimilated 5-day satellite chlorophyll-a (chl-a) concentration and monthly in situ net primary production (NPP) data for 3 years to jointly estimate 10 preselected key BGC parameters and the model state. The estimated set of parameters resulted in improvements in the model prediction up to 66% for the surface chl-a and 56% for NPP. Results show that assimilating satellite chl-a concentration data alone degraded the prediction of NPP. Simultaneous assimilation of the satellite chl-a data and in situ NPP data improved both surface chl-a and NPP simulations. We found that correlations between parameters preclude estimating parameters independently. Co-dependencies between parameters also indicate that there is not a unique set of optimal parameters. Incorporation of proper uncertainty estimation in BGC predictions, therefore, requires ensemble simulations with varying parameter values

Forcing ocean model with atmospheric model outputs to simulate storm surge in the Bangladesh coast

Tropical cyclones are devastating hazards and have been a major problem for the coastal population of Bangladesh. Among the advancements in atmospheric and oceanic prediction, accurate forecasting of storm surges is of specific interest due to their great potential to inflict loss of life and property. For decades, the numerical model based storm surge prediction systems have been an important tool to reduce the loss of human lives and property damage. In order to improve the accuracy in predicting storm surge and coastal inundation, recent model development efforts tended to include more modeling components, such as meteorology model and surface wave model in storm surge modeling. In this study, we used the outputs of an atmospheric model to force the ocean model for simulating storm surges in the Bay of Bengal with particular focus on the Bangladesh coast. The ability of the modeling system was investigated simulating water levels in the Bangladesh coast of two tropical cyclones Sidr (2007) and Aila (2009). The effectiveness of the model was verified through comparing the obtained computational outputs against tide gauge data. The cyclone tracks and intensities reproduced by the atmospheric model were reasonable, though the model had a tendency to overestimate the cyclone intensity during peaks and also close to coast. The water levels are reproduced fairly well by the ocean model, although errors still exist. The root mean square errors in water level at different gauges range from 0.277 to 0.419 m with coefficient of correlation (R2) between 0.64 to 0.97 in case of Sidr and 0.209 to 0.581 m with R2 0.62 to 0.98 for Aila. The overall coupled modeling system is found to be useful with reasonable accuracy and precision, though there are spaces for improvement. Higher-resolution modeling approaches are recommended to gain more skills

Global sensitivity analysis of a one-dimensional ocean biogeochemical model

Ocean biogeochemical (BGC) models are a powerful tool for investigating ocean biogeochemistry and the global carbon cycle. The potential benefits emanating from BGC simulations and predictions are broad, with significant societal impacts from fisheries management to carbon dioxide removal and policy-making. These models contain numerous parameters, each coupled with large uncertainties, leading to significant uncertainty in the model outputs. This study performs a global sensitivity analysis (GSA) of an ocean BGC model to identify the uncertain parameters that impact the variability of model outputs most. The BGC model Regulated Ecosystem Model 2 is used in a one-dimensional configuration at two ocean sites in the North Atlantic (BATS) and the Mediterranean Sea (DYFAMED). Variance-based Sobol' indices are computed to identify the most influential parameters for each site for the quantities of interest (QoIs) commonly considered for the calibration and validation of BGC models. The most sensitive parameters are the chlorophyll to nitrogen ratio, chlorophyll degradation rate, zooplankton grazing and excretion parameters, photosynthesis parameters, and nitrogen and carbon remineralization rate. Overall, the sensitivities of most QoIs were similar across the two sites; however, some differences emerged because of different mixed layer depths. The results suggest that implementing multiple zooplankton function types in BGC models can improve BGC predictions. Further, explicitly implementing heterotrophic bacteria in the model can better simulate the carbon export production and CO2 fluxes. The study offers a comprehensive list of the most important BGC parameters that need to be quantified for future modeling applications and insights for BGC model developments.  </jats:p

Co-producing representations of summer rainfall in Bangladesh

Climate adaptation governance increasingly investigates the cultural capacities of communities to cope with climate variability and change. This paper reports on research of the symbolic representations of summer rainfall in the cultural repertoires guiding diverse institutionalised fields of activity in Sylhet Division. The research conducted interviews and co-created ‘cognitive maps’ with communities, to critically reflect on their changing seasonal symbols. The study revealed a common stock of summer symbols in Sylhet communities, which individuals reconfigure for strategizing and justifying particular practices. Symbols are stable but not static. As people’s uses of knowledge systems change—moving toward scientific representations—so too does their use of symbols. Moreover, environmental and climatic changes, such as a drying summer, are undermining long-held semiotic templates. Many local and traditional signs no longer hold, leaving communities without cultural templates for timely seasonal action. This work highlights the importance of cultural frameworks for organising communities’ seasonal adaptation, and the imperative for critically revisiting frameworks in rapid flux.publishedVersio

Improving Arctic sea-ice thickness estimates with the assimilation of CryoSat-2 summer observations

Rapidly shrinking Arctic sea ice has had substantial impacts on the Earth system. Therefore, reliably estimating the Arctic sea-ice thickness (SIT) using a combination of available observations and numerical modeling is urgently needed. Here, for the first time, we assimilate the latest CryoSat-2 summer SIT data into a coupled ice-ocean model. In particular, an incremental analysis update scheme is implemented to overcome the discontinuity resulting from the combined assimilation of biweekly SIT and daily sea-ice concentration (SIC) data. Along with improved estimates of sea-ice volume, our SIT estimates corrected the overestimation of SIT produced by the reanalysis that assimilates only SIC in summer in areas where the sea ice is roughest and experiences strong deformation, e.g., around the Fram Strait and Greenland. This study suggests that the newly developed CryoSat-2 SIT product, when assimilated properly using our approach, has great potential for Arctic sea-ice simulation and prediction.</jats:p

Current challenges and future directions in data assimilation and reanalysis

The first Joint WCRP1-WWRP2 Symposium on Data Assimilation and Reanalysis took place on13-17 September 2021, and it was organized in conjunction with the ECMWF Annual Seminaron observations. The last WCRP/WWRP-organized meetings were held separately for data assimilation and reanalysis in 2017 (Buizza et al. 2018; Cardinali et al. 2019). Since then, commonchallenges and new emerging topics have increased the need to bring these communities together toexchange new ideas and findings. Thus, a symposium involving the aforementioned communitieswas jointly organized by DWD3, HErZ4, WCRP, WWRP, and the ECMWF annual seminar. Majorgoals were to increase diversity, provide early career scientists with opportunities to present theirwork and extend their professional network, and bridge gaps between the various communities.The online format allowed more than 500 participants from over 50 countries to meet in avirtual setting, using the gathertown 5 platform as the central tool to access the meeting. A virtualconference center was created where people could freely move around and talk to other close-byparticipants. A lobby served as the main hub and it connected the poster halls and the conferencerooms for the oral presentations and the ECMWF seminar talks. The feedback from the participantswas overwhelmingly positive.Scientifically, the meeting offered opportunities to bring together the communities of Earth systemdata assimilation, reanalysis and observations to identify current challenges, seek opportunitiesfor collaboration, and strategic planning on more integrated systems for the longer term. Thecontributions totalled 140 oral and over 150 poster presentations covering a large variety oftopics with increased interest in Earth system approaches, machine learning and increased spatial resolutions. Key findings of the symposium and the ECMWF annual seminar are summarized insection 2. Section 3 highlights the common and emerging challenges of these communities.Fil: Valmassoi, Arianna. Hans-ertel-centre For Weather Research; Alemania. Institut Fur Geowissenschaften ; Universitaet Bonn;Fil: Keller, Jan D.. Deutscher Wetterdienst; AlemaniaFil: Kleist, Daryl T.. National Ocean And Atmospheric Administration; Estados UnidosFil: English, Stephen. European Center For Medium Range Weather Forecasting; Reino UnidoFil: Ahrens, Bodo. Goethe Universitat Frankfurt; AlemaniaFil: Ďurán, Ivan Bašták. Goethe Universitat Frankfurt; AlemaniaFil: Bauernschubert, Elisabeth. Deutscher Wetterdienst; AlemaniaFil: Bosilovich, Michael G.. National Aeronautics and Space Administration; Estados UnidosFil: Fujiwara, Masatomo. Hokkaido University; JapónFil: Hersbach, Hans. European Center For Medium Range Weather Forecasting; Reino UnidoFil: Lei, Lili. Nanjing University; ChinaFil: Löhnert, Ulrich. University Of Cologne; AlemaniaFil: Mamnun, Nabir. Helmholtz Centre for Environmental Research; AlemaniaFil: Martin, Cory R.. German Research Centre for Geosciences; AlemaniaFil: Moore, Andrew. California State University; Estados UnidosFil: Niermann, Deborah. Deutscher Wetterdienst; AlemaniaFil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Scheck, Leonhard. Deutscher Wetterdienst; Alemani

Indoor heat measurement data from low-income households in rural and urban South Asia

Rising temperatures are causing distress across the world, and for those most vulnerable, it is a silent killer. Information about indoor air temperature in residential dwellings is of interest for a range of reasons, such as health, thermal comfort and coping practices. However, there have been only few studies that measure indoor heat exposure, and contrast these to outdoor temperatures in rural-urban areas, of which none are in South Asia. We aim to close this knowledge gap with our indoor and outdoor heat measurement dataset, covering five low-income sites in South Asia. Two sites are in rural areas (Maharashtra, India), while three sites focus on urban areas (Dhaka, Delhi and Faisalabad). Data are based on 206 indoor temperature data loggers and complemented by data from five outdoor automated weather stations. The data-set can be used to examine temperature and humidity variation in low-socioeconomic status households in rural and urban areas and to better understand factors aggravating heat stress. This is important to plan and implement actions for combating heat stress