The goal is to learn how to write better.

分析的目的是为了提高科技英语写作水平(不是针对作者)。

Green highlight is used to show good writing.

好好学习：用绿色标出。

Yellow means "questionable."

敢于提问：用黄色标出。

https://www.frontiersin.org/articles/10.3389/fmars.2019.00623/full?utm_source=F-AAE&utm_medium=EMLF&utm_campaign=MRK_1132190_45_Marine_20191024_arts_A

Observational Needs Supporting Marine Ecosystems Modeling and Forecasting: From the Global Ocean to Regional and Coastal Systems

[1] Many coastal areas host rich marine ecosystems and are also centers of economic activities, including fishing, shipping and recreation.

[2] Due to the socioeconomic and ecological importance of these areas, predicting relevant indicators of the ecosystem state on sub-seasonal to interannual timescales is gaining increasing attention.

[3] Depending on the application, forecasts may be sought for variables and indicators spanning physics (e.g., sea level, temperature, currents), chemistry (e.g., nutrients, oxygen, pH), and biology (from viruses to top predators).

[4] Many components of the marine ecosystem are known to be influenced by leading modes of climate variability, which provide a physical basis for predictability.

[5] However, prediction capabilities remain limited by the lack of a clear understanding of the physical and biological processes involved, as well as by insufficient observations for forecast initialization and verification.

[6] The situation is further complicated by the influence of climate change on ocean conditions along coastal areas, including sea level rise, increased stratification, and shoaling of oxygen minimum zones.

[7] Observations are thus vital to all aspects of marine forecasting: statistical and/or dynamical model development, forecast initialization, and forecast validation, each of which has different observational requirements, which may be also specific to the study region.

[8] Here, we use examples from United States (U.S.) coastal applications to identify and describe the key requirements for an observational network that is needed to facilitate improved process understanding, as well as for sustaining operational ecosystem forecasting.

[9] We also describe new holistic observational approaches, e.g., approaches based on acoustics, inspired by Tara Oceans or by landscape ecology, which have the potential to support and expand ecosystem modeling and forecasting activities by bridging global and local observations.