{ "cells": [ { "cell_type": "markdown", "id": "034f532a", "metadata": {}, "source": [ "# Coordinate transformation from z levels to density levels\n", "\n", "## Rebinning `ty_trans` to `ty_trans_rho` density levels\n", "\n", "This transformation is commonly used for the purpose of decomposing the residual meridional overturning streamfunction into mean and eddy components. The mean is taken to be the time-mean Eulerian transport (time-mean in z coordinates), whilst the residual transport is the time-mean in density coordinates (density surface move in time). The eddy transport is the difference between the residual overturning streamfunction, and the Eulerian transport **transformed from depth to density coordinates** via the time-mean density.\n", "\n", "Binning is the discrete version of this transformation from depth to density coordinates. We define target density bins, and for each bin we add the quantity to be binned from all cells with a density that satisfies that bin. In its simplest form, binning is creating a histogram.\n", "\n", "We use three different binning methods:\n", "1. `xhistogram`, which performs binning in exactly the way MOM5 does and is thus most appropriate for calculating eddy quantities (define edge of bins)\n", "2. `xgcm` conservative binning (define edge of bins, but vertically interpolates so looks smoother than `xhistogram`)\n", "3. Using density coordinate binning method of Lee et al. (2007) (define isopycnals to bin onto i.e. centre)\n", "\n", "Compute times were calculated using the XXLarge (28 cpus, 128 Gb mem) Jupyter Lab on NCI's ARE, using conda environment analysis3-25.05.\n", "\n", "This notebook performs direct coordinate searches using the access-nri intake catalog, and so will __require conda environment analysis3-25.02 or later__." ] }, { "cell_type": "code", "execution_count": 1, "id": "7a700f6b", "metadata": {}, "outputs": [], "source": [ "import intake\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "import glob\n", "import cmocean.cm as cmocean\n", "import xgcm\n", "import logging\n", "logging.captureWarnings(True)\n", "logging.getLogger('py.warnings').setLevel(logging.ERROR)\n", "from xhistogram.xarray import histogram\n", "\n", "from dask.distributed import Client" ] }, { "cell_type": "code", "execution_count": 2, "id": "e049d1c1", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "
\n", "
\n", "

Client

\n", "

Client-760938ed-78ca-11f0-bcd1-00000190fe80

\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
Connection method: Cluster objectCluster type: distributed.LocalCluster
\n", " Dashboard: /proxy/39223/status\n", "
\n", "\n", " \n", " \n", " \n", "\n", " \n", "
\n", "

Cluster Info

\n", "
\n", "
\n", "
\n", "
\n", "

LocalCluster

\n", "

c7e60ff4

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", "
\n", " Dashboard: /proxy/39223/status\n", " \n", " Workers: 48\n", "
\n", " Total threads: 48\n", " \n", " Total memory: 188.56 GiB\n", "
Status: runningUsing processes: True
\n", "\n", "
\n", " \n", "

Scheduler Info

\n", " \n", "\n", "
\n", "
\n", "
\n", "
\n", "

Scheduler

\n", "

Scheduler-290274c1-152d-4e70-a743-71b52afab4ba

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " Comm: tcp://127.0.0.1:46377\n", " \n", " Workers: 0 \n", "
\n", " Dashboard: /proxy/39223/status\n", " \n", " Total threads: 0\n", "
\n", " Started: Just now\n", " \n", " Total memory: 0 B\n", "
\n", "
\n", "
\n", "\n", "
\n", " \n", "

Workers

\n", " \n", "\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 0

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:42971\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/46843/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46371\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-drf8p18c\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 1

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:39343\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39915/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34007\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-p_vlnvkg\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 2

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:33777\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/46341/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36719\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-uo6b5g3_\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 3

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:37869\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34549/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45607\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-5grhk92q\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 4

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:32955\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45685/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34363\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-3gl3yqwt\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 5

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45703\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39415/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33979\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-a0eisfmg\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 6

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:37411\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44969/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39757\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-p15g5f18\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 7

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:37891\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36611/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42259\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-6q2gqxcn\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 8

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:33969\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41859/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34371\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-zkd2dapo\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 9

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45113\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38729/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42165\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-nym4hbvw\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 10

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:42757\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37511/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45667\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-ryz6jadx\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 11

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:43993\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44811/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33903\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-kj0xadq7\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 12

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:37419\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/42689/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41861\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-gas_emjf\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 13

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:43527\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37643/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34015\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-8xr4iu9c\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 14

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45447\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44617/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41637\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-gi88wvyk\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 15

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:34837\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41043/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38065\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-ypwq31ew\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 16

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:43307\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41139/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46883\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-vi1hu9zx\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 17

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:35195\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37811/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46795\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-0v3fn5_u\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 18

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:39475\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43507/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42107\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-7x5_2wrh\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 19

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:37203\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/35863/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42711\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-0uja43o6\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 20

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:38459\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41071/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39099\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-jwocqad5\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 21

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:46749\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/35595/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33397\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-kgt4ywe_\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 22

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:36869\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38675/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35775\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-ne8w49lt\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 23

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:38487\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39215/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41083\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-otinqvdz\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 24

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:40931\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40555/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45663\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-_z1grojk\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 25

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45827\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36651/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36161\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-98voo5y9\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 26

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:39527\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36689/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46611\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-mkpmn3gk\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 27

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:46457\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41227/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:37461\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-m1mtr58g\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 28

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45199\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34297/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33737\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-8c5_x7um\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 29

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:34429\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36851/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39969\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-xsgg7_ra\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 30

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:43693\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/42559/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39439\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-z_8rzeix\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 31

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:42861\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33539/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42773\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-2s6anx3e\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 32

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:33929\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/46813/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39693\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-5bsbjime\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 33

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45377\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38745/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:43553\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-daidlia6\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 34

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:35291\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33717/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:43835\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-o7rh36q9\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 35

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:35687\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/42811/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33971\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-egdtl2pg\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 36

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:46393\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38423/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34669\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-06gwfmeh\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 37

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:37405\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41805/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46409\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-w378gunb\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 38

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:38955\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/42145/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41705\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-j9di77o5\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 39

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45609\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39211/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36463\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-u4vosv5r\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 40

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:41863\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44655/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:43817\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-83lsy_l4\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 41

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:38651\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38923/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:37513\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-u6ilq8yb\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 42

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:43259\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38601/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40733\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-60qh38q8\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 43

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:41307\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40521/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35071\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-fe7aha9e\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 44

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:35969\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34235/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44459\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-qy64vth_\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 45

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:33651\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33019/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38897\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-11m350c4\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 46

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:36687\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44821/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34897\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-rvun1h7s\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 47

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:41615\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41675/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:32863\n", "
\n", " Local directory: /jobfs/147046130.gadi-pbs/dask-scratch-space/worker-qds4lggd\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "2025-08-14 14:54:04,269 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:33651 (pid=130452) exceeded 95% memory budget. Restarting...\n", "2025-08-14 14:54:04,365 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:33651' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 7, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 16, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 7, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 9, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 12, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 4, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 0, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 14, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 8, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 6, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 17, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 5, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 13, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 10, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 9, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 11, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 0, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 2, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 13, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 11, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 15, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 2, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 4, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 7, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 15, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 16, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 9, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 6, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 17, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 3, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 4, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 8, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 1, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 0, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 14, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 6, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 17, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 13, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 12, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 8, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 10, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 10, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 2, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 11, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 5, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 4, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 1, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 16, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 15, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 7, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 6, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 17, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 1, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 8, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 12, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 3, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 14, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 12, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 14, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 3, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 9, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 5, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 16, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 13, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 0, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 11, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 10, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 15, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 2, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 5, 0)} (stimulus_id='handle-worker-cleanup-1755147244.364872')\n", "2025-08-14 14:54:04,844 - distributed.nanny - WARNING - Restarting worker\n", "2025-08-14 14:54:16,617 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:45609 (pid=130438) exceeded 95% memory budget. Restarting...\n", "2025-08-14 14:54:16,692 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:45609' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 7, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 16, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 7, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 9, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 12, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 4, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 0, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 3, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 14, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 8, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 6, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 17, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 5, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 13, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 10, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 9, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 0, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 11, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 2, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 13, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 11, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 15, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 2, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 4, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 7, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 15, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 16, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 5, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 6, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 9, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 14, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 3, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 12, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 1, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 8, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 0, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 4, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 6, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 17, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 13, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 8, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 10, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 2, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 10, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 11, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 5, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 4, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 15, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 16, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 1, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 7, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 6, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 17, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 12, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 1, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 8, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 14, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 3, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 12, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 3, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 14, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 9, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 5, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 13, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 11, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 0, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 10, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 15, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 2, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 16, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 1, 0)} (stimulus_id='handle-worker-cleanup-1755147256.6921506')\n", "2025-08-14 14:54:16,811 - distributed.nanny - WARNING - Restarting worker\n", "2025-08-14 14:54:24,820 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:41863 (pid=130435) exceeded 95% memory budget. Restarting...\n", "2025-08-14 14:54:24,912 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:41863' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 7, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 16, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 9, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 12, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 4, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 14, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 6, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 17, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 13, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 5, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 10, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 11, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 0, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 2, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 13, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 11, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 15, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 2, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 4, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 15, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 7, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 16, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 9, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 17, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 14, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 3, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 12, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 8, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 0, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 1, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 4, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 6, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 17, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 13, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 8, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 10, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 10, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 11, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 16, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 5, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 15, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 4, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 7, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 17, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 6, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 12, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 8, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 1, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 14, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 12, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 14, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 3, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 9, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 5, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 16, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 13, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 11, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 10, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 15, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 2, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 5, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 1, 0)} (stimulus_id='handle-worker-cleanup-1755147264.9121845')\n", "2025-08-14 14:54:24,947 - distributed.nanny - WARNING - Restarting worker\n", "2025-08-14 14:54:32,992 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:42971 (pid=130286) exceeded 95% memory budget. Restarting...\n", "2025-08-14 14:54:33,074 - distributed.scheduler - ERROR - Task finalize-hlgfinalizecompute-d0746d76d09246b981ade5fc459ebecb marked as failed because 4 workers died while trying to run it\n", "2025-08-14 14:54:33,075 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:42971' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 7, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 16, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 7, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 9, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 12, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 4, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 0, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 3, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 14, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 8, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 6, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 9, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 5, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 17, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 13, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 10, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 11, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 0, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 2, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 13, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 15, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 2, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 4, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 15, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 7, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 16, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 17, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 6, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 14, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 3, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 12, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 4, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 0, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 8, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 1, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 6, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 17, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 13, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 8, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 10, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 2, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 11, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 5, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 16, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 1, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 15, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 4, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 7, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 17, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 6, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 12, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 1, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 8, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 14, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 3, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 12, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 3, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 14, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 9, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 5, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 16, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 13, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 0, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 11, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 15, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 2, 1), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 1, 5, 0), ('rechunk-merge-57d0f23514f16e94b8d0dc0f860bba90', 0, 1, 0)} (stimulus_id='handle-worker-cleanup-1755147273.0742443')\n", "2025-08-14 14:54:33,088 - distributed.nanny - WARNING - Restarting worker\n" ] } ], "source": [ "client = Client(threads_per_worker = 1)\n", "client" ] }, { "cell_type": "code", "execution_count": 3, "id": "76feaa74-52a1-4f27-9f1b-557a321de297", "metadata": {}, "outputs": [], "source": [ "experiment = '01deg_jra55v13_ryf9091'\n", "\n", "catalog = intake.cat.access_nri\n", "expt_datastore = catalog[experiment]\n", "\n", "rho_0 = 1035.0 # kg/m^3 reference density\n", "g = 9.81\n", "\n", "# reduce computation by choosing only Southern Ocean latitudes\n", "lat_range = slice(-70, -34.99)" ] }, { "cell_type": "markdown", "id": "74e94e0c-00d9-4552-bb2b-74a7a538a700", "metadata": {}, "source": [ "### Step 1: Load density and quantity being binned \n", "(you can choose any other quantity instead of `ty_trans`, e.g. `dzt`. Interpolate density to whatever grid that variable is on)\n", "\n", "For the simplicity of the demonstrations here, we will **only load two months** of monthly `ty_trans`, `ty_trans_rho` and `pot_rho_2`, then take the time average after we weight with the number of days in each month.\n", "\n", "### Notes on searching for data\n", "- We can search for multiple variables at once: `variable=['ty_trans','ty_trans_rho','pot_rho_2']` will get all three in the same search, which is faster than searching multiple times\n", "- The access-nri intake catalog doesn't support start and end dates yet. To work around this, we use 'regex' (regular expression) to search for dates. This regex: `start_date='2170-0[1-3].*'` says \"find me all strings that start with either '1970-01', '1970-02', or '1970-03'\". For more info on regex's, see [here](https://docs.python.org/3/library/re.html)." ] }, { "cell_type": "code", "execution_count": 4, "id": "bebe18ab-8929-4e8b-985c-551be4f02ea8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6.51 s, sys: 2.07 s, total: 8.58 s\n", "Wall time: 11.7 s\n" ] } ], "source": [ "%%time\n", "start_time = '2170-01-01'\n", "end_time = '2170-03-01'\n", "time_slice = slice(start_time, end_time)\n", "\n", "ds = expt_datastore.search(\n", " variable=['ty_trans','ty_trans_rho','pot_rho_2'],\n", " start_date='2170-0[1-3].*', \n", ").to_dask(xarray_open_kwargs={\n", " 'chunks' : 'auto',\n", "})\n", "\n", "ty_trans, ty_trans_rho, pot_rho_2 = ds['ty_trans'], ds['ty_trans_rho'], ds['pot_rho_2']" ] }, { "cell_type": "code", "execution_count": 5, "id": "99345a56-cc65-44da-8721-aea5467a74ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 32.8 ms, sys: 4.59 ms, total: 37.4 ms\n", "Wall time: 35.6 ms\n" ] } ], "source": [ "%%time\n", "ty_trans = ty_trans.sel(time = time_slice).sel(yu_ocean = lat_range)\n", "\n", "# weighted time-mean by month length\n", "days_in_month = ty_trans.time.dt.days_in_month\n", "total_days = days_in_month.sum()\n", "ty_trans = (ty_trans * days_in_month).sum('time') / total_days" ] }, { "cell_type": "code", "execution_count": 6, "id": "4b5de409-b4d4-4067-a7ab-3f465a0efe1d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 15.5 ms, sys: 0 ns, total: 15.5 ms\n", "Wall time: 14.8 ms\n" ] } ], "source": [ "%%time\n", "\n", "pot_rho_2 = pot_rho_2.sel(time = time_slice).sel(yt_ocean = lat_range)\n", "pot_rho_2 = (pot_rho_2 * days_in_month).sum('time') / total_days" ] }, { "cell_type": "code", "execution_count": 7, "id": "9d40759b-a5de-4827-bfda-490704bc93e0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 22.2 ms, sys: 1.04 ms, total: 23.3 ms\n", "Wall time: 22.2 ms\n" ] } ], "source": [ "%%time\n", "\n", "ty_trans_rho = ty_trans_rho.sel(time = time_slice).sel(grid_yu_ocean = lat_range)\n", "ty_trans_rho = (ty_trans_rho * days_in_month).sum('time') / total_days" ] }, { "cell_type": "markdown", "id": "00cb7a08-4c4a-4742-ad36-6db0e648f434", "metadata": {}, "source": [ "Create an `xgcm` grid for interpolation, and then interpolate density onto the meridional transport grid" ] }, { "cell_type": "code", "execution_count": 8, "id": "eb10c7cd-02e8-4bb5-952e-a4e081606a78", "metadata": {}, "outputs": [], "source": [ "ds = xr.Dataset({'ty_trans': ty_trans, 'pot_rho_2': pot_rho_2}) \n", "grid = xgcm.Grid(ds, coords = {'Y': {'center': 'yt_ocean', 'right': 'yu_ocean'}}, periodic = False)\n", "\n", "# interpolate density (t-grid) to ty_trans grid (xt_ocean and yu_ocean)\n", "pot_rho_2 = grid.interp(pot_rho_2, 'Y', boundary = 'extend')" ] }, { "cell_type": "markdown", "id": "71635773-a64f-4803-b842-4184be71d4e3", "metadata": {}, "source": [ "## Method 1: `xhistogram`\n", "\n", "We use `xhistogram` which is an xarray-aware method for computing histograms; see its [documentation](https://xhistogram.readthedocs.io/en/latest/).\n", "\n", "Computation in xhistogram occurs via the same method as in MOM5 online binning. It is thus most appropriate for an comparisons between offline and online binned quantities.\n", "\n", "### First, we define the edges of the target bins\n", "Output will be an array with coordinate density the linear centre of these bins. If we choose `potrho_edges`, the end result will have coordinates potrho, which is the same as online binned `ty_trans_rho`." ] }, { "cell_type": "code", "execution_count": 9, "id": "5d6ddd90-e0c7-4930-a9d4-26568ba58eee", "metadata": {}, "outputs": [], "source": [ "potrho_edges = expt_datastore.search(\n", " variable='potrho_edges',\n", " start_date='2170-0[1-3].*', \n", " frequency='1mon'\n", ").to_dask(\n", " xarray_open_kwargs= {\n", " 'chunks' : 'auto',\n", " 'decode_timedelta': False,\n", " }\n", ")['potrho_edges']\n", "\n", "targetbins = potrho_edges.values" ] }, { "cell_type": "markdown", "id": "9b43345e-4748-45a7-afbe-3a24f3ad031c", "metadata": {}, "source": [ "### Now apply the histogram over the vertical dimension `st_ocean` inside the target bins. \n", "We include the variable we want to bin in `weights`. This quantity should be extensive, since grid cells vary in sizes, which is true because `ty_trans` is multiplied by the cell size in x and z directions." ] }, { "cell_type": "code", "execution_count": 10, "id": "66f8cc4c-a88b-4111-bed6-7b16a84d6594", "metadata": {}, "outputs": [], "source": [ "# Make sure the variables have a name, otherwise xhistogram doesn't know what to call the bins\n", "ty_trans = ty_trans.rename('ty_trans')\n", "pot_rho_2 = pot_rho_2.rename('pot_rho_2')\n", "\n", "ty_trans_mean = histogram(pot_rho_2, \n", " bins = [targetbins], \n", " dim = ['st_ocean'],\n", " weights = ty_trans).rename({pot_rho_2.name + '_bin': 'potrho',\n", " 'xt_ocean': 'grid_xt_ocean',\n", " 'yu_ocean': 'grid_yu_ocean'})" ] }, { "cell_type": "code", "execution_count": 11, "id": "5f662b5a-9f4f-4e27-b883-66f2002e10b6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-08-14 14:53:22,306 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.95 GiB -- Worker memory limit: 3.93 GiB\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 14 s, sys: 4.82 s, total: 18.9 s\n", "Wall time: 35.6 s\n" ] } ], "source": [ "%%time\n", "ty_trans_mean = ty_trans_mean.load()" ] }, { "cell_type": "markdown", "id": "b2d3e7bd-246a-4c25-a724-8300ff0bbdd9", "metadata": {}, "source": [ "### Now define meridional overturning streamfunctions as a cumulative sum of transport from the bottom of the ocean." ] }, { "cell_type": "code", "execution_count": 12, "id": "919e537e-c139-46ac-bfd1-b599480687f2", "metadata": {}, "outputs": [], "source": [ "def cumsum_from_bottom(residual):\n", " cumsum = residual.cumsum('potrho') - residual.sum('potrho')\n", " return cumsum" ] }, { "cell_type": "code", "execution_count": 13, "id": "380adee2-f0b2-4670-b01d-36ac7ac3cbad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.23 s, sys: 843 ms, total: 3.07 s\n", "Wall time: 2.9 s\n" ] } ], "source": [ "%%time\n", "psi_avg = cumsum_from_bottom(ty_trans_rho.sum('grid_xt_ocean') / (1e6 * rho_0)).load() # .load() since we'll use it a few times in this notebook\n", "\n", "psi_avg_mean = cumsum_from_bottom(ty_trans_mean.sum('grid_xt_ocean') / (1e6 * rho_0))" ] }, { "cell_type": "markdown", "id": "4bbe9e23-c487-4769-a6d1-14f2b3096e75", "metadata": {}, "source": [ "### Plot streamfunctions of the residual, mean (what we just computed) and eddy (the difference) streamfunctions" ] }, { "cell_type": "code", "execution_count": 14, "id": "8f4ed670-6a0e-4b52-a8ec-a6cef918f0f5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 132 ms, sys: 16.4 ms, total: 149 ms\n", "Wall time: 135 ms\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (15, 4))\n", "\n", "levels = np.linspace(-25, 25, 26)\n", "\n", "psi_avg.plot.contourf(ax = axes[0], x = 'grid_yu_ocean', levels = levels, add_colorbar = False)\n", "\n", "psi_avg_mean.plot.contourf(ax = axes[1], x = 'grid_yu_ocean', levels = levels, add_colorbar = False)\n", "\n", "p = (psi_avg - psi_avg_mean).plot.contourf(ax = axes[2], x = 'grid_yu_ocean', levels = levels, add_colorbar = False)\n", "\n", "cbar_ax = fig.add_axes([0.92, 0.15, 0.01, 0.7])\n", "fig.colorbar(p, cax=cbar_ax, label='Transport (Sv)')\n", "\n", "axes[0].set_title('Residual')\n", "axes[1].set_title('Mean')\n", "axes[2].set_title('Eddy')\n", "\n", "for ax in axes:\n", " ax.set_ylim(1037.5, 1032)\n", " ax.set_xlim(-70, -35);" ] }, { "cell_type": "markdown", "id": "eaaafc08-1df8-4e26-9663-624b5fb8cf65", "metadata": {}, "source": [ "## Method 2: `xgcm`\n", "\n", "Use `xgcm`'s conservative binning, described in the [tutorial](https://xgcm.readthedocs.io/en/stable/transform.html) available in the `xgcm` [documentation](https://xgcm.readthedocs.io/en/stable).\n", "\n", "This method results in a smoother vertical distribution than `xhistogram`, as it is not quite a histogram but does some interpolation to the top and bottom of each vertical cell. We have found that the computation currently has issues with interior land boundaries: `xgcm` isn't able to compute partial cells at the bottom of the ocean in MOM5. We thus add a correction after the computation to ensure that vertical integrals are preserved and thus that streamfunctions calculated are closed." ] }, { "cell_type": "markdown", "id": "9e46418e-89b7-449f-bab7-1f259239a55d", "metadata": {}, "source": [ "### Load vertical grid bin centres and edges" ] }, { "cell_type": "code", "execution_count": 15, "id": "964e2221-c7fa-43ec-aea7-8030eb4c9a6c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4.04 s, sys: 1.13 s, total: 5.17 s\n", "Wall time: 6.75 s\n" ] } ], "source": [ "%%time\n", "\n", "ds = expt_datastore.search(\n", " variable=['st_ocean','st_edges_ocean'],\n", " frequency='1mon', # and `frequency` gets us down to a single dataset\n", " start_date='2170-0[1-3].*', \n", ").to_dask(\n", " xarray_open_kwargs={\n", " 'chunks' : 'auto', # This is the easiest way to get a dataset to open relatively quickly\n", " }\n", ")\n", "\n", "st_ocean = ds['st_ocean']\n", "st_edges_ocean = ds['st_edges_ocean']" ] }, { "cell_type": "markdown", "id": "58336fe8-d59d-4489-903a-af25ad35024a", "metadata": {}, "source": [ "### Define edge of target density bins" ] }, { "cell_type": "code", "execution_count": 16, "id": "f11d5d70-c95c-437f-a56a-fdf834fe9aec", "metadata": {}, "outputs": [], "source": [ "ds = expt_datastore.search(\n", " variable='potrho_edges',\n", " frequency='1mon', \n", " start_date='2170-0[1-3].*', \n", ").to_dask(\n", " xarray_open_kwargs={\n", " 'chunks' : 'auto', # This is the easiest way to get a dataset to open relatively quickly\n", " }\n", ")\n", "\n", "\n", "pot_rho_2_target = ds['potrho_edges'].values" ] }, { "cell_type": "markdown", "id": "93d9620f-60be-482a-932a-ebaf72524cce", "metadata": {}, "source": [ "### Calculate vertical regridding into density coordinates" ] }, { "cell_type": "code", "execution_count": 17, "id": "bac76fee-60cf-447e-8ffc-7aa443680285", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 33.1 ms, sys: 0 ns, total: 33.1 ms\n", "Wall time: 31.4 ms\n" ] } ], "source": [ "%%time\n", "\n", "ds = xr.Dataset({'ty_trans': ty_trans, 'pot_rho_2': pot_rho_2})\n", "\n", "# Note: Quantity must be extensive, e.g., ty_trans, vhrho_nt, uhrho_et, tx_trans but *not*, e.g., v, salt. \n", "# If not extensive, then we need to multiply by dzt or dzu.\n", "ds = ds.assign_coords({'st_edges_ocean': st_edges_ocean})\n", "ds = ds.chunk({'st_edges_ocean': 76, 'st_ocean': 75}) # xgcm doesn't like it if there is more than 1 chunk in this axis\n", "\n", "grid = xgcm.Grid(ds, coords={'Z': {'center': 'st_ocean', 'outer': 'st_edges_ocean'}}, periodic = False)\n", "ds['pot_rho_2_outer'] = grid.interp(ds.pot_rho_2, 'Z', boundary='extend')\n", "ds['pot_rho_2_outer'] = ds['pot_rho_2_outer'].chunk({'st_edges_ocean': 76})\n", "\n", "ty_trans_transformed_cons = grid.transform(ds.ty_trans,\n", " 'Z',\n", " pot_rho_2_target,\n", " method='conservative',\n", " target_data=ds.pot_rho_2_outer)\n", "\n", "### change name to fit previous bins because default xgcm naming is different\n", "# pot_rho_2_outer is actually the CENTRE of bins (I guess the convention was defined for linear interpolation not conservative)\n", "ty_trans_transformed_cons = ty_trans_transformed_cons.rename({'pot_rho_2_outer': 'potrho'})" ] }, { "cell_type": "code", "execution_count": 18, "id": "b1719dda-a93c-481e-b75d-a481ec7bafd0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.79 s, sys: 1.74 s, total: 5.53 s\n", "Wall time: 7.8 s\n" ] } ], "source": [ "%%time\n", "ty_trans_transformed_cons = ty_trans_transformed_cons.load()" ] }, { "cell_type": "markdown", "id": "10e2fd59-083b-417b-af83-812d202ce0da", "metadata": {}, "source": [ "### Now account for partial cell at bottom\n", "We do this by finding what is missing from the vertical integral (residual). This is what was in the partial cell. We then add that residual into the densest cell that currently exists in the transformed data. This is only an approximation, because the density of the partial cell may be denser than that of the cell above it. However, this correction means the vertical integral is preserved under the transformation." ] }, { "cell_type": "code", "execution_count": 19, "id": "71a9b5c8-6c18-4aed-9c3e-d23fd470f310", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.25 s, sys: 661 ms, total: 1.91 s\n", "Wall time: 1.28 s\n" ] } ], "source": [ "%%time\n", "#find residual from vertical integral\n", "ty_trans_residual = ty_trans.sum('st_ocean') - ty_trans_transformed_cons.sum('potrho') #this is positive definite" ] }, { "cell_type": "markdown", "id": "115208ff-07d1-45f4-bcfb-6e7a0290de7a", "metadata": {}, "source": [ "### Find bottom density of `ty_trans_transformed_cons`" ] }, { "cell_type": "code", "execution_count": 20, "id": "e2d07412-6377-4fe5-be5f-530b3ea8b87d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.68 s, sys: 970 ms, total: 2.65 s\n", "Wall time: 1.77 s\n" ] } ], "source": [ "%%time\n", "# select out bottom values:\n", "ty_trans_transformed_cons2 = ty_trans_transformed_cons.where(ty_trans_transformed_cons != 0) \n", "dens_array = ty_trans_transformed_cons2 * 0 + ty_trans_transformed_cons2.potrho # array of isopycnal value where it exists and nan elsewhere\n", "max_dens = dens_array.max(dim = 'potrho', skipna = True)" ] }, { "cell_type": "markdown", "id": "1096b59c-b87e-47cb-a291-6eb479020d5d", "metadata": {}, "source": [ "### Add residual to this bottom density in array" ] }, { "cell_type": "code", "execution_count": 21, "id": "4af3a264-5894-4d9d-afbe-7341b6697073", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.86 s, sys: 1.54 s, total: 4.39 s\n", "Wall time: 2.92 s\n" ] } ], "source": [ "%%time\n", "ty_trans_residual_array = (dens_array.where(dens_array == max_dens)*0 + 1) * ty_trans_residual\n", "ty_trans_new = ty_trans_residual_array.fillna(0)+ ty_trans_transformed_cons" ] }, { "cell_type": "code", "execution_count": 22, "id": "2ce959e0-2c92-47be-bb64-88d78408fe59", "metadata": {}, "outputs": [], "source": [ "# rename coords to match ty_trans_rho\n", "ty_trans_new = ty_trans_new.rename({'yu_ocean': 'grid_yu_ocean', 'xt_ocean': 'grid_xt_ocean'})" ] }, { "cell_type": "code", "execution_count": null, "id": "e5294573-f880-4a9e-8347-23c7d3045e6d", "metadata": {}, "outputs": [], "source": [ "%%time\n", "# We need to rechunk here to make the chunks a bit smaller, otherwise the dask workers will die.\n", "# I've halved the chunk size on `grid_xt_ocean` and `potrho`. Sometimes, {'chunks' : 'auto'} can be\n", "# a bit too ambitious!\n", "\n", "ty_trans_new = ty_trans_new.chunk(\n", " chunks = {\n", " 'grid_yu_ocean': 337,\n", " 'grid_xt_ocean': 200, # was 400\n", " 'potrho': 40, # was 80\n", " }\n", ")\n", "ty_trans_new = ty_trans_new.load()" ] }, { "cell_type": "markdown", "id": "75c9d270-47d1-45c1-8796-32944e6b2fb3", "metadata": {}, "source": [ "### Plot streamfunction of result" ] }, { "cell_type": "code", "execution_count": 24, "id": "8ea61ffe-980d-45b9-823d-2db8422ea105", "metadata": {}, "outputs": [], "source": [ "psi_avg_mean_2 = cumsum_from_bottom(ty_trans_new.sum('grid_xt_ocean') / (1e6 * rho_0))" ] }, { "cell_type": "code", "execution_count": 25, "id": "6535bf5b-83d4-40ba-8296-0618ba4366e9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-08-14 14:54:45,788 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.63 GiB -- Worker memory limit: 3.93 GiB\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10.1 s, sys: 5.92 s, total: 16 s\n", "Wall time: 22.7 s\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "\n", "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (15, 4))\n", "\n", "levels = np.linspace(-25, 25, 26)\n", "\n", "psi_avg.plot.contourf(ax = axes[0], x = 'grid_yu_ocean',levels = levels, add_colorbar = False)\n", "\n", "psi_avg_mean_2.plot.contourf(ax = axes[1], x = 'grid_yu_ocean', levels = levels, add_colorbar = False)\n", "\n", "p = (psi_avg - psi_avg_mean_2).plot.contourf(ax = axes[2], x = 'grid_yu_ocean', levels = levels, add_colorbar = False)\n", "\n", "cbar_ax = fig.add_axes([0.92, 0.15, 0.01, 0.7])\n", "fig.colorbar(p, cax=cbar_ax, label='Transport (Sv)')\n", "\n", "axes[0].set_title('Residual')\n", "axes[1].set_title('Mean')\n", "axes[2].set_title('Eddy')\n", "\n", "for ax in axes:\n", " ax.set_ylim(1037.5, 1032)\n", " ax.set_xlim(-70, -35);" ] }, { "cell_type": "markdown", "id": "ce8152ad-8c28-467a-b867-fe7408a01023", "metadata": { "tags": [] }, "source": [ "## Method 3: Bin `ty_trans` (mean Eulerian overturning) into density bins\n", "\n", "This uses the method by Lee et al. (2007) and which is is described as follows. Firstly, cells in the model output with a density $\\rho$ between two prescribed densities ($\\rho_\\text{heavy} >\\rho> \\rho_\\text{light}$) are selected. Each cell is assigned a proximity to the lighter density $\\rho_\\text{light}$, which is the 'bin fraction' $$f_b = \\frac{\\rho_\\text{heavy}-\\rho}{\\rho_\\text{heavy}-\\rho_\\text{light}}.$$\n", "\n", "Here, a bin fraction of 1 means the cell density $\\rho = \\rho_\\text{light}$ and $f_b=0$ means $\\rho = \\rho_\\text{heavy}$. The quantity being binned, such as the meridional transport $vh$, is then multiplied by $f_b$ and added to the lighter density $\\rho_\\text{light}$ bin's meridional transport, followed by the $vh(1-f_b)$ being added to the heavier bin, $\\rho_\\text{heavy}$. This process is repeated for all sets of consecutive bins, meaning that density bins have input from model output cells with density slightly lower and higher than it. \n", "\n", "> Lee, M., Nurser, A., Coward, A., and De Cuevas, B. (2007). [Eddy advective and diffusive\n", "transports of heat and salt in the Southern Ocean.](https://doi.org/10.1175/JPO3057.1) _Journal of Physical Oceanography_, **37(5)**, 1376–1393." ] }, { "cell_type": "markdown", "id": "063607a5-1bd7-4c8f-8cc1-0d89bf2f02d0", "metadata": {}, "source": [ "### For this method, we need to get rid of NaNs" ] }, { "cell_type": "code", "execution_count": 26, "id": "fed405b2-ff26-47cc-96d5-e8326aeafa57", "metadata": {}, "outputs": [], "source": [ "ty_trans = ty_trans.fillna(0)\n", "pot_rho_2 = pot_rho_2.fillna(0)" ] }, { "cell_type": "markdown", "id": "1645e9d1-f09f-4e3e-b087-383ec4bfb6a6", "metadata": { "tags": [] }, "source": [ "### Load first to reduce computation time" ] }, { "cell_type": "code", "execution_count": 27, "id": "e105a23e-9179-4e73-aa26-93d1dd1056f9", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6.25 s, sys: 3.05 s, total: 9.3 s\n", "Wall time: 13.6 s\n" ] } ], "source": [ "%%time\n", "pot_rho_2 = pot_rho_2.load()\n", "ty_trans = ty_trans.load()" ] }, { "cell_type": "code", "execution_count": 28, "id": "bdb54ac7-92e9-48f3-b20a-d07810f2625d", "metadata": {}, "outputs": [], "source": [ "# choose bins (in this case, default ty_trans_rho pot_rho_2 bins\n", "rho2_bins = ty_trans_rho.potrho.values\n", "\n", "# set up a zero array to be filled in by the algorithm\n", "ty_trans_binned = np.zeros((len(rho2_bins), len(pot_rho_2.yu_ocean), len(pot_rho_2.xt_ocean)))" ] }, { "cell_type": "markdown", "id": "e830e801-f679-4630-af97-814159fa59d3", "metadata": {}, "source": [ "**Note**: the following cell takes a while." ] }, { "cell_type": "code", "execution_count": 29, "id": "42324e61-50c2-4cb7-8ed3-b9533ab0f4a3", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "667d22af225f4547aa8f198304fa7011", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/79 [00:00 rho2_bins[i])*0 + 1\n", " bin_fractions = (rho2_bins[i+1] - pot_rho_2 * bin_mask) / (rho2_bins[i+1] - rho2_bins[i])\n", " ## bin ty_trans:\n", " ty_trans_in_lower_bin = (ty_trans * bin_mask * bin_fractions).sum(dim = 'st_ocean')\n", " ty_trans_binned[i, :, :] += ty_trans_in_lower_bin.fillna(0).values\n", " del ty_trans_in_lower_bin\n", " ty_trans_in_upper_bin = (ty_trans * bin_mask * (1 - bin_fractions)).sum(dim = 'st_ocean')\n", " ty_trans_binned[i+1, :, :] += ty_trans_in_upper_bin.fillna(0).values\n", " del ty_trans_in_upper_bin" ] }, { "cell_type": "code", "execution_count": 30, "id": "398b5155-b1f8-4da3-ad31-adfcbbd918d2", "metadata": {}, "outputs": [], "source": [ "# convert numpy array into xarray dataarray\n", "ty_trans_binned_array = xr.DataArray(ty_trans_binned, \n", " coords = [rho2_bins, pot_rho_2.yu_ocean, pot_rho_2.xt_ocean], \n", " dims = ['potrho', 'grid_yu_ocean', 'grid_xt_ocean'], \n", " name = 'ty_trans_binned')" ] }, { "cell_type": "code", "execution_count": 31, "id": "690ddd63-0ffc-497b-8dbe-bed29d59d980", "metadata": {}, "outputs": [], "source": [ "# find streamfunction\n", "psi_avg_mean_3 = cumsum_from_bottom(ty_trans_binned_array.sum('grid_xt_ocean')) / (1e6 * rho_0)" ] }, { "cell_type": "markdown", "id": "35ddc3b8-5d15-413e-8cb6-d5e8073841f8", "metadata": {}, "source": [ "### Plot streamfunctions" ] }, { "cell_type": "code", "execution_count": 32, "id": "58a7fe12-4fcd-49ca-a553-e7f4e185212c", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (15, 4))\n", "\n", "levels = np.linspace(-25, 25, 26)\n", "\n", "psi_avg.plot.contourf(ax = axes[0], x = 'grid_yu_ocean',levels = levels, add_colorbar = False)\n", "\n", "psi_avg_mean_3.plot.contourf(ax = axes[1], x = 'grid_yu_ocean', levels = levels, add_colorbar = False)\n", "\n", "p = (psi_avg - psi_avg_mean_3).plot.contourf(ax = axes[2], x = 'grid_yu_ocean', levels = levels, add_colorbar = False)\n", "\n", "cbar_ax = fig.add_axes([0.92, 0.15, 0.01, 0.7])\n", "fig.colorbar(p, cax = cbar_ax, label = 'Transport (Sv)')\n", "\n", "axes[0].set_title('Residual')\n", "axes[1].set_title('Mean')\n", "axes[2].set_title('Eddy')\n", "\n", "for ax in axes:\n", " ax.set_ylim(1037.5, 1032)\n", " ax.set_xlim(-70, -35);" ] }, { "cell_type": "markdown", "id": "4b64145b-4b4c-4480-972a-51112c466756", "metadata": { "tags": [] }, "source": [ "### All three methods yield similar plots of the decomposition of the meridional overturning streamfunction. \n", "\n", "The exact numbers resulting from the transformations differ, due to the different numerical methods. `xhistogram` is exact when compared to snapshots of MOM5 model output, and is hence most appropriate when comparing between online and offline binned quantities. `xgcm` and the Lee method can still be used to compare quantities binned offline. For example, if we were to instead bin the daily transports onto isopycnals and calculate eddy terms directly from the correlation of velocity and thickness fluctuations, `xgcm` and the Lee method may be better as the weighted binning onto isopycnals leaves less chance of 'gaps' between density layers. The Lee method has no issues with partial cells, but it is **much slower** than `xgcm`.\n", "\n", "The difference in the mean streamfunction between the three methods is presented below." ] }, { "cell_type": "code", "execution_count": 33, "id": "8d5f00b8-a2de-4328-b665-195eb75c2842", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-08-14 15:07:34,392 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.72 GiB -- Worker memory limit: 3.93 GiB\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (15, 4))\n", "\n", "levels = np.arange(-10, 10.1, 0.5)\n", "\n", "(psi_avg_mean - psi_avg_mean_2).plot.contourf(ax = axes[0], x = 'grid_yu_ocean',levels = levels, add_colorbar = False)\n", "\n", "(psi_avg_mean - psi_avg_mean_3).plot.contourf(ax = axes[1], x = 'grid_yu_ocean', levels = levels, add_colorbar = False)\n", "\n", "p = (psi_avg_mean_2 - psi_avg_mean_3).plot.contourf(ax = axes[2], x = 'grid_yu_ocean', levels = levels, add_colorbar = False)\n", "\n", "cbar_ax = fig.add_axes([0.92, 0.15, 0.01, 0.7])\n", "fig.colorbar(p, cax=cbar_ax, label='Transport (Sv)')\n", "\n", "axes[0].set_title('Method 1 - Method 2')\n", "axes[1].set_title('Method 1 - Method 3')\n", "axes[2].set_title('Method 2 - Method 3')\n", "\n", "for ax in axes:\n", " ax.set_ylim(1037.5, 1032)\n", " ax.set_xlim(-70, -35)\n", "\n", "fig.suptitle('Difference in Mean Streamfunction', fontsize = 16);" ] }, { "cell_type": "markdown", "id": "eed3d00b-224e-448d-8cbd-c8198244fe87", "metadata": {}, "source": [ "An alternative method to calculate the decomposition of the residual meridional overturning circulation into mean and eddy components is to bin `ty_trans` (or `vhrho_nt`) into density bins using daily data (both density and transport). Then the transport $\\overline{vh}$ can be separated into a mean component $\\overline{v}\\overline{h}$ and an eddy component $\\overline{v^\\prime h^\\prime}$, where the overline is a time average and primed quantities the deviation from the time average. Quantities are calculated within density layers, and $h$ is density layer thickness, calculated by binning `dzt` or `dzu`. This calculation, since it uses daily data, is computationally expensive and is difficult to do efficiently in a Jupyter notebook. The resulting streamfunctions are similar, but not identical, and may be more intuitive for isopycnal flows. An example of this method can be found at https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Figure_Code/Fig5-Overturning.ipynb." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 5 }