Background#
Author: Minh Phan (UW Varanasi intern 2023)
This dataset is a 1972-2022 daily data cube with a spatial range of −12◦S → 32◦N, 42◦E → 102◦E. Where necessary, we applied linear interpolation on, both spatially and temporally, on all of our data variables so that they all follow an average daily temporal, 0.25◦ × 0.25◦ spatial grid.
The dataset is in Zarr format. Zarr, short for “zarr array,” is a storage format specifically designed for efficient, scalable, and parallelizable access to multi-dimensional typed arrays (tensors), making it an ideal choice for managing Earth observation data, especially for cloud-hosted data (cite gowen (Moore et al., 2023). It is developed as an open-source project by using referencible associated metadata and binary data called “chunks” stored in “formatted” directories, it leverages modern data storage technologies, such as chunked and compressed arrays, to optimize storage and retrieval, and reducing access latency.
Documentation on how the Zarr dataset was created.
Data Sources#
Most of our remote sensing data are sourced from the Copernicus program. Copernicus program is an European flagship program providing reliable and open satellite-based imagery, models, and in situ (nonspace) data, and is a coordinated effort between many organizations, including the European Commission, the European Space Agency, the European Centre for Medium-Range Weather Forecasts (ECMWF), and European Union Agencies (Skoda & Adam, 2020). Furthermore, we also blended data from the National Aeronautics and Space Administration (NASA) EarthData and the National Centers for Environmental Information (NCEI)’s databases.
ERA5#
The primary data source we used in this assembled dataset product is the Copernicus ERA5 Global Reanalysis, the fifth generation of an atmospheric reanalysis project from Copernicus and ECMWF (Hersbach et al., 2020). ERA5 aims to provide a “detailed record of the global atmosphere, land surface, and ocean waves from 1950 onwards” (Hersbach et al., 2020). However, only 1979 data onwards is currently publicly available for download. The dataset’s high temporal and spatial resolution and ranges enables consistent, detailed, and concise detection and prediction tasks in our project. We compared ERA5 to another popular reanalysis product, MERRA-2 by NASA. ERA5 was choosen since has better resolution, time coverage, and accuracy (Olauson, 2018). We used five variables from ERA5: sea surface temperature (sst), two-meter-high from surface level atmospheric temperature (air_temp), and surface wind velocities (u_wind and v_wind).
Sea surface temperature#
The temperature of the ocean’s surface, known as Sea Surface Temperature (SST), serves as a crucial gauge for assessing the Earth’s climate system (Reynolds et al., 2002). Consequently, having precise information about SST is vital for monitoring, researching, and forecasting climate patterns. In the case of coastal upwelling, as seasonally low SST compared to the temperature off-shore at the same latitude is indicative of coastal upwelling (Benazzouz et al., 2014; Alvarez et al., 2010; Izumo et al., 2008) as the deep cold water is pulled to the surface by the upwelling forces.
Atmospheric temperature#
Air temperature does not have a direct correlation with upwelling as SST does; however, research has shown that upwelling strength may be influenced by air temperature records in the preceding seasons before the upwelling season (Sun et al., 2022).
Vertical and horizontal components of the surface wind#
One of the prominent coastal upwelling characteristics is a parallel wind direction along the coast (Lill, 1978). Strong winds can also cool the ocean surface, promoting the conditions and occurrence of upwelling (Kim et al., 2023). Longshore surface wind is also a major factor in mass transport and upwelling intensity, especially in the case of wind stress (Nigam et al., 2018).
Global Ocean Physics Reanalysis (GLORY)#
The Global Ocean Physics Reanalysis (product code name GLORYS12V1) is the first version of a Copernicus Marine Environment Monitoring Service (CMEMS)’s reanalysis product covering an 5000m elevation range from 1993, with models used for reanalysis similar to ERA5’s (European Union-Copernicus Marine Service, 2018). It covers a wide range of variables relevant to our project. Salinity (so) and mean mixed layer thickness (mlotst) were extracted from this data source. The GLORY data were down-sampled using arithmetic mean from the original 1/12◦ resolution to our 0.25◦ resolution (Jean-Michel et al., 2021).
Salinity#
We used in-situ salinity covered at the most shallow point on the elevation range at 0.49 meters below the surface with bias reduced using 3D-VAR scheme correction (Jean- Michel et al., 2021). During coastal upwelling in the West Indian Ocean, subsurface water, which is more salined, rises up to the surface, bringing additional salinity to the surface water which is diluted by heavy precipitation in the monsoon season (Awo et al., 2022; Sreenath et al., 2022). Note that this is not always the case. For example, the Coast of Bengal, where river discharges and rainfall combined created a thick barrier and shallow mixed layer, preventing salinity to reach the surface (Vinayachandran et al., 2002; Lahiri and Vissa, 2022). Despite that, coastal upwelling still occurs in the area with the aid from the impact of seasonal monsoon (Ray et al., 2022).
Mixed Layer Thickness Defined by Sigma T#
During our search of possible variables to add in our blended dataset product, we came accross Bessa et al. (2019)’s paper in which the author discovered that there is a month by month variability in the mixed layer depth that coincides with the trend of upwelling and downwelling in the Moroccan Atlantic coast. Further research found speculation towards the relationship between the two variables in the Arabian Sea of our region of interest. In Copernicus terms, it is defined as “the ocean depth at which sigma-theta has increase by 0.01km/m3 relative to the near surface value at 10m depth” (Mladek, 2019). Sigma-delta is defined as “water potential density (the density when moved adiabatically to a reference pressure) of water having the same temperature and salinity, minus 1000 kg m-3” (CF Conventions, n.d.).
Global Ocean Colour (Copernicus-GlobColour)#
The Ocean Colour Thematic Assembly Centre (OCTAC) currently provide global and regional high quality data products used by mostly intergovermnetal bodies and EU institutions, focusing on mostly ecosystem model assimilation and validation (European Union Copernicus Marine Service, 2022). The Global Ocean Colour dataset (code name OCEAN-COLOUR GLO BGC L4 MY 009 104), based on data validated using the GlobColour processor owned by Copernicus, output daily and monthly data on a 4km × 4km spatial resolution covering data from September 1997.
Gapfree chlorophyll-a concentration and uncertainty (Level 4)#
When upwelling happens, we can also observe an increase in nutrient-rich near-surface waters (Benazzouz et al., 2014), in which wind (convective) mixing and upward nutrient fluxes to the subsurface zone leads to phytoplankton bloom and chlorophyll-a production (Lahiri and Vissa, 2022; Brock et al., 1991). Cold waters being mixed rise above the thermocline to the surface, promoting the growth of species in unfavorable environments, which also contains chlorophyll-a (Alvarez et al., 2010). Therefore, high chlorophyll-a concentrations at the sea surface level can imply whether upwelling is happening. However, there is still missing data in this ‘gap-free’ product, which is also confirmed in Park et al. (2020)’s paper. Many factors are weighed in, including phytoplankton’ photosyntehtic parameters, seawater optical complexity, or flog and clouds peristence due to seasonal monsoon, leading to rain. S. Yu et al. (2022)’s dataset, while addressed this issue, does not issue a daily resolution dataset that we need to incorporate into our product.
Global Ocean Gridded L4 Sea Surface Heights And Derived Variables Reprocessed 1993 Ongoing#
The dataset (code name SEALEVEL GLOB PHY L4 MY 008 047) is part of the Sea Level Altimeter product family, providing multiyear records of sea surface height anomalies and derived variables for the whole global ocean (European Union-Copernicus Marine Service, 2021). It has a 0.25◦ × 0.25◦ spatial resolution, similar to the standard ERA5’s that we based on, and covers the temporal range from 1993 to 2022. DOI: https://doi.org/10.48670/moi-00148
Sea surface height above geoid and sea surface height above level (sea level height anomaly)#
Wind components may be a good starting point to investigate the state of coastal upwelling, but sea surface height anomaly is more directly involved through changes in the the thermocline and isothermal layer depth changes (Zhang and Mochizuki, 2022; L. Yu, 2003). We have been searching to no avail for public D20 (20◦ isothermal layer depth) or D20 anomaly dataset that satisfies our resolution and coverage requirements. Zhang and Mochizuki (2022) calculated this variable using monthly ocean temp data, but no specific formulae/method is disclosed. We resort to sea surface height anomalies data as the variable is somewhat related to the former variable itself, albeit not completely linear due to complex involvements of other variables in our blended data product, like salinity and temperature (L. Yu, 2003).
Ocean Surface Current Analyses Real-time (OSCAR) Version 2.0#
OSCAR uses sea surface height anomaly from the above dataset to compute and analyze the surface current components we are using in our product (Dohan, 2021). In fact, the Global Ocean Gridded L4 dataset also included those variables; however, since we discovered OSCAR first, we have already processed this data before knowing the existence of the other one.
Vertical and horizontal components of surface currents and geostrophic surface currents#
There are many papers discussing the correlation of currents, especially surface currents, and coastal upwelling (Lentz and Chapman, 2004; Nigam et al., 2018). Rao et al. (2008) mentioned how certain surface current directions (in the case of West Indian Ocean, southernly) would be favourable to the phenomenon. Somalia currents, the surface current in the western Arabian Sea of our interested region, has positive influences to upwelling (Schott et al., 1990). Geostrophic currents, defined as currents balanced between the Coriolis effect forces and gradient pressure forces, are found to contribute greatly to upwelling in systems such as the California Current Systems or Arafura Sea in Indonesia (Ding et al., 2021; Umaroh et al., 2017). By including both non-geostrophic and geostrophic currents, we hope to identify how using the two difference types of currents may impact the model’s productivity, indicating whether it is better to use one variable or the other, or both.
SRTM30+ Global 1-km Digital Elevation Model Version 11: Bathymetry#
A product from the Scripts Institution of Oceanography of the University of California San Diego, the SRTM30 Plus’s bathymetry data is based on a satellite-gravity model with a heavily calibrated gravity-to-topography ratio from over two hundred millions soundings (Becker et al., 2009). Given the ultra high resolution and precision of the map, we subsetted and provided two bathymetry maps covering our region of interest with different resolutions, one with standard 0.25◦ × 0.25◦ included in the Zarr files and another one with finer resolution using as a basemap/map background due to any graphing of the variables.
Bathymetry#
There have been multiple studies on the relationship between coastal upwelling, such as Garvine (1973) or Lill (1978). They proposed that the ocean depth (and inherently the ocean floor shape or bathymetry) can determine the motion of the subsurface return flow, one of the two principle layers of the upwelling motion of homogeneous water. The topographic variation also influences the water circulation, such as disrupting or redirecting flows along coasts, weakening them, or increasing their strength to enhance mixing (Pitcher et al., 2010).
Computed Variables#
We pre-computed absolute speed and direction using u- and v- components of our wind and (near-)surface current variables. For speed, we utilized a simple Pythagorean theorem approach where
with \(v\) as vector-less speed, and \(v_x\) and \(v_y\) as horizontal and vertical velocity components, respectively. For direction, we utilized NumPy’s arctan2() function and then convert radians to degrees using their rad2deg() function, with the latter chosen as degrees are more commonly used in meteorology than radians due to its unique conventions comparing to the standard mathematical Cartesian plane’s (Harris et al., 2020; “Meteorological Concentions”, 2022).