Hwa-Jin Choi

Validation of Arctic river runoff simulated by Community Land Model (CLM4.5) with R-ArcticNet dataset

hjchoi0620 — Tue, 2 Apr 2024 22:03:08 +0900

Why we are interested in river discharge into the Arctic?

The future phytoplankton change influenced by greenhouse warming can amplify Arctic surface warming considerably(Park et al., 2015).

Freshwater inflow into the Arctic Ocean is important to the thermohaline circulation of the world’s oceans, Arctic Ocean circulation, salinity, sea ice dynamics, and biomass of phytoplankton.
Shrinking Beaufort gyre and enhanced transpolar drift by Eurasian river discharge.

Method & Data

	Point ID	Station	Latitude	Longitude	Drainage area (km2)
N/A	3660	Mackenzie at arctic red river	67.45	-133.74	1,680,000
Eurasia	7142	Ob at Salekhard	66.63	66.60	2,950,000
Eurasia	6656	Yenisei at lgarka	67.43	86.48	2,440,000

Discharge into ocean (NDJFM)

Yenisei: 50-75°N, 80-105°E, Ob: 50-75°N, 60-90°E

Mackenzie: 50-75 °N,105-135 °W

Chlorophyll concentration comparison between Ocean BGC models and satellite data

hjchoi0620 — Tue, 2 Apr 2024 21:47:19 +0900

Chlorophyll concentration comparison – TOPAZ and MODIS

Chlorophyll concentration comparison – TOPAZ and BEC

CESM BEC(Biogeochemical Elemental Cycling) model

The Community Earth System Model (CESM) is a fully-coupled, global climate model. (composed of separate models simultaneously simulating atmosphere, ocean, land, land-ice, and sea-ice.)
The BEC model is an ecosystem and biogeochemical module that runs within the CCSM POP2 ocean circulation model.
Key Model Components:

Four phytoplankton functional groups
One adaptive zooplankton class
Key limiting nutrients (N, P, Fe, Si), plus C, O, and alkalinity
Dissolved Organic Matter
Sinking Particulates (Organic, Si, CaCO3, Dust)
Includes atmospheric deposition of nitrogen and iron.
CCSM4 POP2 ocean circulation model, ~1 degree resolution with 60 vertical levels

Validation of historical CESM(BGC) with PIOMAS

3. Results and Summary - Seasonally distinct contributions of greenhouse gases and anthropogenic aerosols to historical changes in Arctic moisture budget

hjchoi0620 — Mon, 1 Apr 2024 23:46:31 +0900

Long-term trends

Figure 1. Simulated 5-year mean anomalies in Arctic precipitation, evaporation, and poleward moisture transport across 70 °N and long-term trends for 1960-2019 Time series of 5-year mean area-averaged anomalies of a precipitation, c evaporation, and e poleward moisture transport from CMIP6 multi-model simulations with anthropogenic plus natural (ALL, green), only greenhouse gas (GHG, red), anthropogenic aerosol (AER, purple), natural forcing (NAT, blue) over Arctic region (70°-90°N) during 1960-2019. The summed results of GHG, NAT and AER (GHG+NAT+AER, grey) are also displayed. Shadings indicate inter-model ranges for each experiment. Long-term trends in b precipitation (P), d evaporation (E), and f poleward moisture transport (F = P-E). Black dots indicate individual model values.

ALL : P and E show a slight decrease in the 1960-1970s due to the cooling effect of anthropogenic aerosol and, subsequently a strong increase.
GHG : P, E, and F (P-E) monotonically increase.
AER : P and E exhibit a decrease until the 1990s and then slightly increase (Tokarska et al., 2020), reflecting changes in aerosol forcing during this period.

Spatial pattern of long-term trends

Figure 2. Geographic distribution of long-term trends in precipitation, evaporation, moisture transport convergence, and sea-ice cover in the Arctic region Spatial patterns of long-term trends in precipitation (P), evaporation (E), convergence of moisture transport (P - E), and sea-ice concentration (SIC) during 1960-2019 from CMIP6 multi-model simulations with ALL, GHG, NAT, and AER. GHG+NAT+AER patterns are also displayed for comparison. Gray dots show regions of low inter-model agreement where less than 75% (six out of eight) have the same sign of trends.

ALL and GHG: precipitation increases in most regions.
Larger evaporation increases over the Barents-Kara sea due to sea-ice retreat.
AER : precipitation and evaporation decrease especially over the northern North Pacific.

Transport and evaporation contribution to precipitation change

Figure 3. Monthly changes in poleward moisture transport across 70°N and surface evaporation components during 1960-2019 Each bar represents the monthly and multi-model mean. Total Arctic surface evaporation is separated into ice-retreat (red), ice-advance (yellow), and sea-ice non-related (orange) components. Green asterisks (*) indicate net Arctic precipitation changes (i.e., sum of four bars).

ALL and GHG have similar seasonal variations of F and E, consistent with Bintanja et al. (2014): summer peak of transport changes, and winter-spring peak of evaporation.
The evaporation contribution due to sea-ice retreat is stronger in GHG than in ALL.
Summer: the poleward moisture flux explains 92%(75%) of precipitation changes.
Late autumn and winter: surface evaporation accounts for 77%(89%) of the precipitation increase.

Long-term trends in zonal averaged MMF, MMC, SE, and TE

Figure 4. Long-term trends of zonally averaged meridional moisture flux (MMF), mean meridional circulation (MMC), stationary eddies (SEs), and transient eddies (TEs) for 1960-2019 MMF is the sum of MMC, SE, and TE in each latitude. The green, red, blue, and purple lines represent ALL, GHG, NAT, and AER simulations, respectively.

MMF, SE, and TE tend to increase overall in ALL and GHG. At 70°N, the contribution of moisture transport components to the increase in MMF is slightly different between ALL and GHG.
In ALL, the change of TE is responsible for most (109%) of the MMF change at 70°N, whereas contributions of MMC and SE are -32% and 23%, respectively.
In GHG, contributions of TE, MMC, and SE are 69%, -19%, and 50%, respectively, indicating an increased importance of SE.

Monthly changes of meridional moisture fluxes across 70°N

Figure 5. Monthly trends of MMF, MMC, SE, and TE across 70°N during 1960-2019 MMF is divided into contributions of MMC, SE, and TE. Asterisks indicate that at least five out of six models have the same sign.

Asterisks: good inter-model agreement (All models have the same sign)
ALL and GHG: MMF increase for all months. In summer and autumn, TE is dominant for poleward moisture transport.
In the change of TE, ALL is larger than GHG for most months.

Relations between changes in Arctic precipitation and changes in the MMF, MMC, SE and TE transport across 70°N

Figure 6. Relations between Arctic precipitation change and meridional moisture flux and its components Scatter plots showing inter-model relations between changes in precipitation (70-90°N average) and MMF and its components (across 70°N). Linear regression line and correlation coefficients with corresponding p-values based on all 24 models (black) are presented.

GHG and ALL results are generally located close to each other with larger amplitudes compared to NAT and AER. This results in statistically significant linear relationships between Arctic precipitation change and changes in MMF, SE, and TE.

Spatial patterns of seasonal changes of MMF during 1960-2019

Figure 7. Spatial patterns of seasonal changes in MMF during 1960-2019 Gray dots indicate areas of low inter-model agreement where less than 75% of the models (five out of six) have the same sign. The black line depicts 70°N.

Trends of MMF in the Arctic vary considerably across regions. Iceland and Norway have a large variability of MMF in the Atlantic sector.
GHG runs show stronger trends than other forcings. Although ALL and GHG differ in the intensity of trends, their regional patterns are quite similar. The MMF in the Norwegian Sea increases strongly in all seasons.
In contrast, Alaska and Northeastern Russia (120°W-120°E) in the Pacific sector have different responses according to individual forcings. In this region, there is a difference between GHG and AER, evident in autumn and winter. The poleward MMF in AER decreases in Alaska but increases in Northeastern Russia.

Regional difference of TE change across 70°N

Figure 8. Trends in monthly TE across 70°N in the Pacific sector and Atlantic sector during 1960-2019 TE is separated into that of the Atlantic sector (magenta) and Pacific sector (light blue). Black asterisks (*) indicate total TE changes across 70°N, which are the sum of two sectors in each month.

In ALL, TE increase in the Pacific sector is stronger (explaining 60% of total change) than that of the Atlantic sector during summer, but the Atlantic contribution becomes dominant during autumn (explaining 79%)
ALL is larger than GHG for all months.
In summer, GHG exhibits a weaker contribution of the Atlantic than ALL.

TE change in summer across 70°N

Pacific sector: 90°E - 90°W
Atlantic sector: 90°W - 90°E
In summer, ALL and GHG share increases in TE over the Beaufort Sea (160°W-120°W).
In summer, a strong TE increase over western Eurasia (0°-90°E) is observed in ALL, which explains in part a greater contribution of the Atlantic sector to the change of TE in ALL than in GHG.

Summary

We examine the past changes in Arctic moisture budget for 1960-2019 using CMIP6 historical simulations with different external forcing factors.

(1) Long-term changes in Arctic moisture budget

Arctic precipitation and evaporation increase in ALL and GHG while they decrease in AER.
In ALL and GHG, increases in Arctic precipitation in summer are attributed to enhanced poleward moisture inflow while the increases in winter are due to the intensified local surface evaporation in sea-ice retreat regions.
In AER, Arctic precipitation tends to decrease during cold seasons due to reduced evaporation over sea-ice advance regions.

(2) Long-term changes in poleward moisture transport

In summer and early autumn, TE is a main driver of the increased poleward moisture transport with weaker contributions from the MMC and SE changes in ALL and GHG.
In ALL and GHG, MMF increased the most in summer. Because it is the summer and early autumn in which the humidity increases the most.
In summer, the increase of TE in ALL is greater than that of GHG. This is found to be due to the greater contribution of the Atlantic sector, particularly western Eurasia, than the Pacific.

Reference

Choi, HJ., Min, SK., Yeh, SW. et al. Seasonally distinct contributions of greenhouse gases and anthropogenic aerosols to historical changes in Arctic moisture budget. npj Clim Atmos Sci 6, 189 (2023). https://doi.org/10.1038/s41612-023-00518-9

2. Data and Method - Seasonally distinct contributions of greenhouse gases and anthropogenic aerosols to historical changes in Arctic moisture budget

hjchoi0620 — Mon, 1 Apr 2024 23:24:38 +0900

Data

CMIP6 individual forcing simulations

ALL (historical + ssp2-4.5) : Natural & anthropogenic forcing
GHG (hist-GHG) : Greenhouse gases forcing only
NAT (hist-nat) : Natural forcing
AER (hist-aer) : Aerosol forcing

	Moisture budget analysis	Vertically integrated meridional moisture flux
Analysis Period	1960-2019	1960-2019
Frequency	Monthly	Daily
Vertical Level	Surface	1000, 850, 700,500,250 (hPa)
Variable	PR, TAS, HFLS	Q, V, PS

TAS : 2m air temperature
HFLS : Surface Upward Latent Heat Flux
PR : Precipitation

Model	Number of simulations
BCC-CSM2-MR	1
CanESM5*	10(10*)
CNRM-CM6-1*	6(3*)
HadGEM3-GC31-LL	3
IPSL-CM6A-LR*	5(5*)
MIROC6	3
MRI-ESM2-0*	3(3*)
NorESM2-LM	3
8 models(4 models*)	34 runs(21 runs*)

* : Models used to analyze vertically integrated meridional moisture flux

Method

Moisture budget analysis

P  : The total Arctic precipitation (downward)
F  : The poleward atmospheric moisture transports
E  : The Arctic surface evaporation (upward)

(Bintanja and Selten, 2014)

The Arctic atmosphere moisture reservoir is quite small. The mean residence time of atmospheric moisture is less than one week. ∂Q/∂t ≪ F-(P-E)
The Arctic region is defined as 70°-90°N.

Components for surface evaporation changes

sea-ice retreat region: SIC of 2010-2019 < 50% & the fractional change < -30%
sea-ice advance region: SIC of 2010-2019 > 50% & the fractional change > +30%
unrelated to sea-ice

Fractional change : the difference between the means over 2010-2019 and 1960-1969

Vertically integrated meridional moisture flux

MMC : Mean meridional circulation.It is related to the Polar cell causing southwards flow near the surface and northwards flow aloft.
SE : Stationary eddy.It represent moisture transport due to deviations of specific humidity and wind speed from zonal mean.
TE : Transient eddy. It is related to synoptic-scale transient cyclones. It is responsible for the majority of northwards net moisture transport. (Jakobson et al., 2010)

Regional difference analysis of eddy components across 70°N

Pacific sector : 90°E - 90°W
Atlantic sector : 90°W - 90°E

Reference

1. Introduction - Seasonally distinct contributions of greenhouse gases and anthropogenic aerosols to historical changes in Arctic moisture budget

hjchoi0620 — Mon, 1 Apr 2024 23:12:20 +0900

Important impacts of intensified Arctic atmospheric water cycle

Changes in the Arctic freshwater system are likely to have a range of effects.
P – E changes have direct impacts on the salinity of the Arctic Ocean, which is a critical factor in determining ocean convection, affecting the Atlantic Meridional Overturning Circulation (AMOC).
Changes in the Arctic freshwater system influence the terrestrial carbon cycle. (Wrona et al., 2016)

Previous studies

Future increases in Arctic precipitation linked to local evaporation and sea-ice retreat

(Bintanja and Selten, 2014)

In the future scenarios, moisture increases due to
: moisture transports (maximum in summer), local surface evaporation (maximum in winter)
Many studies have analyzed future projections compared to historical simulations

Attribution of Arctic temperature change to greenhouse-gas and aerosol influences

(Najafi et al., 2015)

Aerosol-induced cooling offsets 1.3 to 2.2°C of GHG-induced warming during the past decades.

How much do the external forcings contribute to the Arctic moisture budget changes?

Objectives

Analyze the hydrological changes in the Arctic over the past decades using CMIP6 individual forcing experiments.

Reference

2. Results - Prediction skill of Metabolic Index and its application to fish catch prediction

hjchoi0620 — Mon, 1 Apr 2024 22:55:57 +0900

Results

Metabolic Index prediction skill

Figure 1. Predictability of metabolic index. (A-C) Prediction skill of the normalized metabolic index at 0-100 m depth, with values of (A) 0.2, (B), 0.7, and (C) 1.2. (D-F) and (G-I) are similar to (A-C), but at 100-300 m and 300-800 m depth, respectively. The prediction skill is measured by the mean annual anomaly correlation coefficient between reanalyzed data and lead-time forecasts during 1991-2017. Black dots denote statistically significant values at the 95% confidence level.

The predictive skill of the metabolic index shows significant skill in many ocean regions in one year advance.
The skillful prediction of the metabolic index extends to a 2-year lead time across many global oceans, particularly within the subsurface layers. Nevertheless, the predictive skill is generally reduced compared to that of a 1-year lead time.
The notable decrease in predictive skill above the 300 m depth appears in the tropical Pacific. This limited skill is attributed to the inherent challenges dynamic models encounter in predicting interannual climate variability, such as El Niño-Southern Oscillation (ENSO), beyond a one-year horizon.

Prediction skill difference (Metabolic index – Temperature)

Figure 2. Differences in prediction skill between the metabolic index and temperature. (A-C) Differences in prediction skill between the normalized metabolic index and temperature at 0-100m, with values of (A) 0.2, (B), 0.7, and (C) 1.2. (D-F) and (G-I) are similar to (A-C), but at 100-300 m and 300-800 m depth, respectively. The prediction skill is measured by the mean annual anomaly correlation coefficient between reanalyzed data and lead-time forecasts during 1991-2017. Positive values indicate that the oxygen prediction skill is higher than that of temperature.

Differences in the correlation between ECDA-COBALT and forecast for MI and T
The higher “MI – Temp” -> better oxygen predictability than temperature predictability.
The positive difference in prediction skill, indicating an increased predictive benefit due to oxygen, appears primarily in the tropical Pacific region.

Subsurface oxygen and temperature budget analysis over the tropical region

Figure 3. Subsurface oxygen and temperature budget analysis and the persistency of the advection terms over the tropical region. Timeseries of reanalyzed subsurface (100-300m) (A) oxygen and (B) temperature advection over the tropical region (150°E-90°W,20°S-20°N) for 1991-2017. Red lines show the lateral advections which are the sum of anomalous zonal and meridional advections. Blue lines indicate the anomalous vertical advections. Black, purple, and yellow lines indicate the anomalous time tendencies, the sum of lateral and vertical advections, and the residual, respectively. The numbers in the brackets denote the correlation coefficient between the time tendency term and each term. Asterisks mean statistically significant at the 95% confidence level. Persistency of the advections in subsurface (C) oxygen and (D) temperature. Red and blue bars indicate the persistency of the lateral and the vertical advection terms, respectively. Significance at 95% level confidence is indicated by black dashed lines.

The oxygen budget analysis indicates that the lateral advection is responsible for the oxygen anomalies over the tropical region, whereas the temperature is strongly influenced by vertical advection.
The persistence of lateral oxygen advection which controls the temporal oxygen variability is larger than the temperature advections, suggesting that the oxygen reveals a longer memory than the temperature.
The difference in the main drivers of oxygen and temperature variability arises from distinct characteristics in the spatial patterns of their climatology. The areas of higher predictability of oxygen relative to temperature are located along the periphery of the oxygen minimum zone (OMZ) in the eastern Pacific, defined by deficient oxygen concentrations (< 20-45 mmol kg-1).
Horizontal inflow across the strong horizontal gradient of oxygen concentration plays a dominant role in influencing oxygen variations compared to vertical advection. In contrast to the oxygen, interannual variability of temperature in the tropical Pacific is primarily affected by the vertical fluctuations in thermocline depth, largely caused by phenomena like the ENSO.

Application of Metabolic Index to predict bigeye tuna

Figure 4. Predictability of Bigeye tuna using metabolic index in the tropical area where oxygen is well predicted. (A) Hatched area is a region where oxygen is well predicted, which means the difference between Metabolic Index and temperature predictability of more than 0.25. In the hatched area, the six EEZs were selected. The EEZs include Galapagos Islands (219), Kiribati (942), Palmyra Atoll & Kingman Reef (844), Jarvis Islands (845), Marshall Islands (584), and Micronesia (583). (B) Black line shows detrended reported annual catches of Bigeye tuna in EEZs where oxygen is well predicted. Green, red, and blue lines indicate the reanalyzed, the 1-year lead-time, and 2-year lead-time forecasts of Metabolic Index, respectively. Asterisks denote the significant (P < 0.05) correlation between reported catches and metabolic indexes.

The Bigeye tuna catch was detrended to lessen the potential human error of fishing effort trends onto environmental elements.
Metabolic index was applied to predict bigeye. Bigeye tuna catches could be predicted for up to one year using the Metabolic Index.

1. Introduction, Data & method - Prediction skill of Metabolic Index and its application to fish catch prediction

hjchoi0620 — Mon, 1 Apr 2024 21:12:46 +0900

Introduction

Impacts of climate change on marine ecosystems

Marine ecosystems have been facing unprecedented climate change as rising temperatures, declines in dissolved O2, and rapid ocean acidification.
Climate change has caused shifts in the abundance, geographic distribution, and phenology of marine species.

Importance of dissolved oxygen in marine ecosystems

https://sitn.hms.harvard.edu/flash/2020/climate-change-the-oceans-mood-killer/

There has been a lot of work on the role of temperature on these shifts, but less on other factors (e.g., oxygen)
Warmer ocean temperatures occur with a decrease in dissolved oxygen, potentially limiting biological aerobic abilities.
Oxygen and temperature are essential factors in identifying biological geographic distribution current and in the future.
(Increasing sea temperature affects DO through different processes and mechanisms, including DO solubility in the ocean, ocean stratification, ventilation of seawater, and respirations of consumers including bacteria, zooplankton, and fish )
Previous metabolic studies have focused on physical variables such as temperature rather than oxygen.

Metabolic Index (Deutsch et al.,2015)

: combined effects of temperature and oxygen to estimate marine habitat change

Objectives

We use a recently ecosystem prediction from earth system model(Park et al.,2019) to assess the predictability of the metabolic index in comparison with temperature/oxygen predictability.

Data and Method

Normalized Metabolic Index

EXP05, chl_clim experiment (2012)

hjchoi0620 — Mon, 1 Apr 2024 17:28:19 +0900

Serious error

I met two significant errors for running the model, which is the instability error and the “p4zopt.f90 ” crash issue.

Try 1) nn_date0=20120101, rn_rdt = 3600s, boundary forcing ⇒ monthly, Crashed due to grid resolution on April 16th

Try 2) 20120101, 3600s until March ⇒ From April 1st 2400s ⇒ Crashed on April 8th

Try 3) when 1800s, Crashed on April 10th ⇒ Resolution is too small, error occurs and no progress

Try 4) Run to 1800s until April 8th, starting April 9th with 2400s, Crashed due to grid problems on April 10th

Try 5) Try compiling without -fbound-check.; nemo2

1800s	Crashed on April 10th, grid issue?
1600s	Crashed on April 10th, grid issue?
1200s	Crashed on April 10th
900s	Crashed on April 10th

Try 6) Grid resolution problem in nemo2 starting from early April at 2400s, decreasing from 1800s to 600s also failed.

Try 7) nemo2; From January, 3600s ⇒ On April 16th, crashed due to grid resoluton

Try 8) nemo2; From January to April 15, 3600s ⇒ 2400s, 1800s, … 400s crashed

Try 9) nemo2; From January to April 10, 3600s

Try 10) nn_date0=20120101 → until on Feburary 29th

rn_rdt = 5400s, boundary forcing ⇒ monthly ⇒ crashed due to grid resolution (unstable)

Try 11) nn_date0=20120101 → until on Feburary 29th → until the end of April → crashed on March 20th

rn_rdt = 3600s

Try 12) rn_rdt = 2400s → crashed on March 20th

Try 13) rn_rdt = 1800s → crashed on March 19th due to grid

Try 14) rn_rdt = 3600s → Run only to January

Try 15) rn_rdt = 4320s → Run only from January, crashed on April 16th

Try 16) rn_rdf = 4320s → Runs until March 31st, from April

BOUNDARY DATA

hjchoi0620 — Mon, 1 Apr 2024 16:08:05 +0900

Boundary data needed

I used JRA55 for running the experiments.

The contents below are the names and information of the JRA data used.

JRA55

anl_surf125

spfh : Specific humidity (6 hours)

vgrd : v-component of wind (6 hours)

ugrd : u-component of wind (6 hours)

tmp : 2m Temperature (6 hours)

prmsl : mean sea level (6 hours)

fcst_phy2m125

tprat : Total precipitation (monthly)

dlwrf : Downward longwave radiation flux (daily)

dswrf : Downward solar wave radiation flux (daily)

srweq : Solid precipitation (monthly)

Reference

Gurvan Madec, Mike Bell, Adam Blaker, Clément Bricaud, Diego Bruciaferri, Miguel Castrillo, Daley Calvert, Jérômeme Chanut, Emanuela Clementi, Andrew Coward, Italo Epicoco, Christian Éthé, Jonas Ganderton, James Harle, Katherine Hutchinson, Doroteaciro Iovino, Dan Lea, Tomas Lovato, Matt Martin, … Chris Wilson. (2023). NEMO Ocean Engine Reference Manual. In Scientific Notes of IPSL Climate Modelling Center (v4.2.1, Number 27). Zenodo. https://doi.org/10.5281/zenodo.8167700

Useful sites and materials to run the NEMO model

hjchoi0620 — Mon, 1 Apr 2024 15:32:11 +0900

I had a hard time running the NEMO model on my own.
But thanks to good websites, I was able to run the model successfully.
For those of you who need these websites, I am sharing a list of good websites.

Basic Manuals

NEMO Ocean Engine Reference Manual

NEMO Ocean Engine Reference Manual

The ocean engine of NEMO is a primitive equation model adapted to regional and global ocean circulation problems. It is intended to be a flexible tool for studying the ocean and its interactions with the others components of the earth climate system over a

zenodo.org

TOP – Tracers in Ocean Paradigm – The NEMO Tracers engine

NEMO TOP Working Group. (2018). TOP – Tracers in Ocean Paradigm – The NEMO Tracers engine. In Notes du Pôle de modélisation de l'Institut Pierre-Simon Laplace (IPSL) (v4.2.0, Number 28). Zenodo. https://doi.org/10.5281/zenodo.1471700

TOP – Tracers in Ocean Paradigm – The NEMO Tracers engine

Reference manual for NEMO Tracers engine

zenodo.org

SI3, the NEMO Sea Ice Engine

Vancoppenolle, M., Rousset, C., Blockley, E., Aksenov, Y., Feltham, D., Fichefet, T., Garric, G., Guémas, V., Iovino, D., Keeley, S., Madec, G., Massonnet, F., Ridley, J., Schroeder, D., & Tietsche, S. (2023). SI3, the NEMO Sea Ice Engine (4.2release_doc1.0). Zenodo. https://doi.org/10.5281/zenodo.7534900

SI3, the NEMO Sea Ice Engine

SI3 (Sea Ice modelling Integrated Initiative) is the sea ice engine of NEMO (Nucleus for European Modelling of the Ocean). It is adapted to regional and global sea ice and climate problems. It is intended to be a flexible tool for studying sea ice and its

zenodo.org

Personal websites

I am sharing some really useful websites that I referenced when running the NEMO model!! Thanks to these websites, I was able to run the model successfully. I appreciate them.

* Websites that were very helpful in building an environment to run NEMO

NEMO 4.0 (beta) + XIOS 2.5 — J Mak NEMO notes documentation

There is a restructuring of folders (see the official annoucement for details) so the commands below will reflect this. Check out a version of NEMO. I have another folder separate to the XIOS folders to contain the NEMO codes and binaries: This checks out

nemo-related.readthedocs.io

https://afstyles.github.io/nemo-compilation/

Compiling NEMO 4.0.1 on Monsoon/NEXCS | Andrew Styles

A guide to compiling NEMO 4.0 on Monsoon/NEXCS.

afstyles.github.io

https://salishsea-meopar-docs.readthedocs.io/en/latest/code-notes/salishsea-nemo/nemo-forcing/atmospheric.html

Atmospheric Forcing — Salish Sea MEOPAR documentation

[Smith_etal2013] Smith, G. C., Roy, F., Mann, P., Dupont, F., Brasnett, B., Lemieux, J.-F., Laroche, S. and Bélair, S. (2013), A new atmospheric dataset for forcing ice–ocean models: Evaluation of reforecasts using the Canadian global deterministic pred

salishsea-meopar-docs.readthedocs.io

https://github.com/fmidev/nemo-gof/blob/master/EXP00/namelist_cfg

nemo-gof/EXP00/namelist_cfg at master · fmidev/nemo-gof

Gulf of Finland configuration for the NEMO ocean model - fmidev/nemo-gof

github.com

https://github.com/NOC-MSM/SANH/wiki/8.-Running-NEMO

8. Running NEMO

NEMO configuration for the SANH model domain . Contribute to NOC-MSM/SANH development by creating an account on GitHub.

github.com

NEMO community

* Community website with many questions about errors that occur while running NEMO

https://nemo-ocean.discourse.group/

NEMO Community Ocean Model

nemo-ocean.discourse.group