diff --git a/backend/src/ref_backend/models.py b/backend/src/ref_backend/models.py index 7f0b06d..98d9a68 100644 --- a/backend/src/ref_backend/models.py +++ b/backend/src/ref_backend/models.py @@ -554,6 +554,13 @@ class Facet(BaseModel): values: list[str] +# ``kind`` is a CV dimension but is surfaced as a dedicated ``kind`` field on each value and +# excluded from the per-item ``dimensions`` mapping (see climate_ref ScalarValue.dimensions). +# Offering it as a facet would let callers filter on a key that never appears in item dimensions, +# so it is excluded to keep facets a subset of the item dimensions. +NON_FACET_DIMENSIONS = frozenset({"kind"}) + + @define class AnnotatedScalarValue: value: models.ScalarMetricValue @@ -623,7 +630,11 @@ def build_scalar_from_reader( for item in collection.items ] - facets = [Facet(key=f.key, values=list(f.values)) for f in collection.facets] + facets = [ + Facet(key=f.key, values=list(f.values)) + for f in collection.facets + if f.key not in 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Model evaluation with focus on the cloud radiative effect.

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Plot lwcre zonal mean including reference datasets.

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Projects

References

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Ecs: Calculate

">
- Global annual mean top of the atmosphere (TOA) net radiation flux anomaly N vs. global annual mean near-surface air temperature anomaly ΔT for 25 years (blue circles) of the abrupt 4x CO2 experiment for model ACCESS-ESM1-5 (ensemble member r1i1p1f1). Anomalies are calculated relative to a pre-industrial control simulation of the same model. The solid black line corresponds to an ordinary linear regression between N and ΔT. On the top left, R2 corresponds to the coefficient of determination of the linear regression. The equilibrium climate sensitivity (ECS) is estimated as the x-intercept of the linear regression (interception of the solid and dashed black lines) divided by 2. + Global annual mean top of the atmosphere (TOA) net radiation flux anomaly N vs. global annual mean near-surface air temperature anomaly ΔT for 150 years (blue circles) of the abrupt 4x CO2 experiment for model ACCESS-ESM1-5 (ensemble member r1i1p1f1). Anomalies are calculated relative to a pre-industrial control simulation of the same model. The solid black line corresponds to an ordinary linear regression between N and ΔT. On the top left, R2 corresponds to the coefficient of determination of the linear regression. The equilibrium climate sensitivity (ECS) is estimated as the x-intercept of the linear regression (interception of the solid and dashed black lines) divided by 2.
- Global annual mean top of the atmosphere (TOA) net radiation flux anomaly N vs. global annual mean near-surface air temperature anomaly ΔT for 25 years (blue circles) of the abrupt 4x CO2 experiment for model ACCESS-ESM1-5 (ensemble member r1i1p1f1). Anomalies are calculated relative to a pre-industrial control simulation of the same model. The solid black line corresponds to an ordinary linear regression between N and ΔT. On the top left, R2 corresponds to the coefficient of determination of the linear regression. The equilibrium climate sensitivity (ECS) is estimated as the x-intercept of the linear regression (interception of the solid and dashed black lines) divided by 2. + Global annual mean top of the atmosphere (TOA) net radiation flux anomaly N vs. global annual mean near-surface air temperature anomaly ΔT for 150 years (blue circles) of the abrupt 4x CO2 experiment for model ACCESS-ESM1-5 (ensemble member r1i1p1f1). Anomalies are calculated relative to a pre-industrial control simulation of the same model. The solid black line corresponds to an ordinary linear regression between N and ΔT. On the top left, R2 corresponds to the coefficient of determination of the linear regression. The equilibrium climate sensitivity (ECS) is estimated as the x-intercept of the linear regression (interception of the solid and dashed black lines) divided by 2.

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Zonal_Mean_Profiles

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Zonal Mean Profiles: Plot

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+ + Zonal mean profile of Mole Fraction of O3 of dataset CMIP6. + +
+ Zonal mean profile of Mole Fraction of O3 of dataset CMIP6. +
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+ download | + references | + extra data citation | + provenance +
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Files

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backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/esmvaltool/climate-drivers-for-fire/cb9d6f6dcaacca6d9b6a1803cabd555cea97d2d5/executions/recipe_20251001_162136/index.html rename to backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/esmvaltool/regional-historical-trend/gn_r1i1p1f1_ACCESS-ESM1-5_g26_v1_a18aa09b/13/executions/recipe_20260716_140605/index.html index e94fdfb..3eba410 100644 --- a/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/esmvaltool/climate-drivers-for-fire/cb9d6f6dcaacca6d9b6a1803cabd555cea97d2d5/executions/recipe_20251001_162136/index.html +++ b/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/esmvaltool/regional-historical-trend/gn_r1i1p1f1_ACCESS-ESM1-5_g26_v1_a18aa09b/13/executions/recipe_20260716_140605/index.html @@ -92,22 +92,22 @@
-

Climate drivers for fire.

+

Linear trends.

- Recipe that plots diagnostics regarding climate drivers for fire. The -diagnostic uses code for the ConFire model from the following GitHub -repository: -https://github.com/douglask3/Bayesian_fire_models/tree/AR7_REF + Box plot, linear trends for the 46 IPCC AR6 reference regions for land +(Iturbide et al., 2020). The diagnostic called in this recipe uses the +seaborn.boxplot function. See seaborn manual for detailed information +(https://seaborn.pydata.org/generated/seaborn.boxplot.html).

Authors

@@ -115,7 +115,7 @@

Maintainers

@@ -168,7 +168,23 @@

References

+ + + + + + + + @@ -177,13 +193,13 @@

References

-

Fire_Evaluation

-

Climate drivers for fire

+

Tas_Trends

+

-

Fire Evaluation: Fire Evaluation

+

Tas Trends: Plot

@@ -191,59 +207,135 @@

Fire Evaluation: Fire Evaluation

">
- - Burnt area fraction for the ACCESS-ESM1-5 (CMIP6-historical) for the time period 2013/2014 as computed with the ConFire model (Jones et al., 2024). + + Seaborn barplot for one or more dataset(s)
- Burnt area fraction for the ACCESS-ESM1-5 (CMIP6-historical) for the time period 2013/2014 as computed with the ConFire model (Jones et al., 2024). + Seaborn barplot for one or more dataset(s)

- download | - references | - extra data citation | - provenance + download | + references | + extra data citation | + provenance
+ +

Data files

+ +
+
+ +
+
+ + + + + + + +
+

Pr_Trends

+

+ + + + +

Pr Trends: Plot

+ + +
- - Fire weather control for the ACCESS-ESM1-5 (CMIP6-historical) for the time period 2013/2014 as computed with the ConFire model (Jones et al., 2024). + + Seaborn barplot for one or more dataset(s)
- Fire weather control for the ACCESS-ESM1-5 (CMIP6-historical) for the time period 2013/2014 as computed with the ConFire model (Jones et al., 2024). + Seaborn barplot for one or more dataset(s)

- download | - references | - extra data citation | - provenance + download | + references | + extra data citation | + provenance
+ +

Data files

+ +
+
+ +
+
+ + + +
+ + + +
+

Psl_Trends

+

+ + + + +

Psl Trends: Plot

+ + +
- - Fuel load continuity control for the ACCESS-ESM1-5 (CMIP6-historical) for the time period 2013/2014 as computed with the ConFire model (Jones et al., 2024). + + Seaborn barplot for one or more dataset(s)
- Fuel load continuity control for the ACCESS-ESM1-5 (CMIP6-historical) for the time period 2013/2014 as computed with the ConFire model (Jones et al., 2024). + Seaborn barplot for one or more dataset(s)

- download | - references | - extra data citation | - provenance + download | + references | + extra data citation | + provenance
@@ -251,19 +343,137 @@

Fire Evaluation: Fire Evaluation

-

Data files

+

Data files

-
+
+
+ +
+
+ + + +
+ + + +
+

Ua200_Trends

+

+ + + + +

Ua200 Trends: Plot

+ + + +
+
+ + Seaborn barplot for one or more dataset(s) + +
+ Seaborn barplot for one or more dataset(s) +
+
+ download | + references | + extra data citation | + provenance +
+
+
+ + + + +

Data files

+ +
+
+ +
+
+ + + +
+ + + +
+

Hus200_Trends

+

+ + + + +

Hus200 Trends: Plot

+ + + +
+
+ + Seaborn barplot for one or more dataset(s) + +
+ Seaborn barplot for one or more dataset(s) +
+
+ download | + references | + extra data citation | + provenance +
+
+
+ + + + +

Data files

+ +
  • - | - download | - references | - extra data citation | - provenance + Seaborn barplot for one or more dataset(s) | + download | + references | + extra data citation | + provenance
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    Tcre

    -

    Tcre: Calculate Tcre

    +

    Tcre: Calculate

    @@ -233,29 +233,17 @@

    Tcre: Calculate Tcre

    ">
    - - Global annual mean near-surface air temperature anomaly ΔT vs. -global annual cumulative CO2 emissions E of the emission-driven 1% -CO2 increase per year experiment for model MPI-ESM1-2-LR (ensemble -member r1i1p1f1). The transient climate response to cumulative CO2 -Emissions (TCRE) is defined as the 20-year average ΔT centered at -the time where cumulative CO2 emissions E reach 1000 PgC. - + + Global annual mean near-surface air temperature anomaly ΔT vs. global annual cumulative CO2 emissions E of the emission-driven 1% CO2 increase per year experiment. The transient climate response to cumulative CO2 Emissions (TCRE) is defined as the 20-year average ΔT centered at the time where cumulative CO2 emissions E reach 1000 PgC (i.e., after 55 years).
    - Global annual mean near-surface air temperature anomaly ΔT vs. -global annual cumulative CO2 emissions E of the emission-driven 1% -CO2 increase per year experiment for model MPI-ESM1-2-LR (ensemble -member r1i1p1f1). The transient climate response to cumulative CO2 -Emissions (TCRE) is defined as the 20-year average ΔT centered at -the time where cumulative CO2 emissions E reach 1000 PgC. - + Global annual mean near-surface air temperature anomaly ΔT vs. global annual cumulative CO2 emissions E of the emission-driven 1% CO2 increase per year experiment. The transient climate response to cumulative CO2 Emissions (TCRE) is defined as the 20-year average ΔT centered at the time where cumulative CO2 emissions E reach 1000 PgC (i.e., after 55 years).

    - download | - references | - extra data citation | - provenance + download | + references | + extra data citation | + provenance
@@ -271,17 +259,21 @@

Data files - -
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Scalar Table

-
-
- - - -
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timeseries

- - - - - - - -
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- - - - - -
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b/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/historical_gn_r1i1p1f1_ACCESS-ESM1-5_g36_v1_dcfcc801/19/diagnostic.json @@ -0,0 +1,150 @@ +{ + "DIMENSIONS": { + "experiment_id": { + "historical": {} + }, + "grid_label": { + "gn": {} + }, + "json_structure": [ + "kind", + "reference_source_id", + "experiment_id", + "source_id", + "member_id", + "grid_label", + "region", + "metric", + "statistic" + ], + "kind": { + "model": {} + }, + "member_id": { + "r1i1p1f1": {} + }, + "metric": { + "Annual Cycle": {}, + "Bias": {}, + "RMSE": {}, + "Spatial Distribution": {} + }, + "reference_source_id": { + "FLUXNET2015": {} + }, + "region": { + "global": { + "Description": "Global - Land", + "Generator": "GlobalLand.nc", + "LongName": "Global - Land" + }, + "tropical": { + "Description": "Tropical climates", + "Generator": "Koppen_coarse.nc", + "LongName": "Tropical climates" + } + }, + "source_id": { + "ACCESS-ESM1-5": {} + }, + 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a/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/e2a5786432e4ca6860602bcee6a982137da5e166/index.html b/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/historical_gn_r1i1p1f1_ACCESS-ESM1-5_g36_v1_dcfcc801/19/index.html similarity index 71% rename from backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/e2a5786432e4ca6860602bcee6a982137da5e166/index.html rename to backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/historical_gn_r1i1p1f1_ACCESS-ESM1-5_g36_v1_dcfcc801/19/index.html index 163bfcf..f044e56 100644 --- a/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/e2a5786432e4ca6860602bcee6a982137da5e166/index.html +++ b/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/historical_gn_r1i1p1f1_ACCESS-ESM1-5_g36_v1_dcfcc801/19/index.html @@ -6,7 +6,8 @@ gpp-fluxnet2015 - + + - @@ -239,6 +245,17 @@ +
+ + +
@@ -251,7 +268,7 @@
- + @@ -318,6 +335,41 @@
+
  • + +
    Title
    +Fluxnet2015 gpp +     +
    • + +
  • + +
    Variable_id
    +gpp +     +
    • + +
  • + +
    Institution
    +The Fluxnet Community +     +
    • + +
  • + +
    Version
    +v20260202 +     +
    • + +
  • + +
    Doi
    +N/A +     +
    • +
    @@ -408,13 +460,14 @@
    - +
    diff --git a/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/e2a5786432e4ca6860602bcee6a982137da5e166/output.json b/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/historical_gn_r1i1p1f1_ACCESS-ESM1-5_g36_v1_dcfcc801/19/output.json similarity index 67% rename from backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/e2a5786432e4ca6860602bcee6a982137da5e166/output.json rename to backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/historical_gn_r1i1p1f1_ACCESS-ESM1-5_g36_v1_dcfcc801/19/output.json index aa4c29d..e0b0a17 100644 --- a/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/e2a5786432e4ca6860602bcee6a982137da5e166/output.json +++ b/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/historical_gn_r1i1p1f1_ACCESS-ESM1-5_g36_v1_dcfcc801/19/output.json @@ -6,11 +6,30 @@ "obsdata": {}, "log": "cmec_output.log" }, - "data": {}, + "data": { + "ACCESS-ESM1-5.csv": { + "filename": "ACCESS-ESM1-5.csv", + "long_name": "ACCESS-ESM1-5.csv", + "description": "Scalar and time-series data produced by ILAMB.", + "dimensions": null + }, + "ACCESS-ESM1-5.nc": { + "filename": "ACCESS-ESM1-5.nc", + "long_name": "ACCESS-ESM1-5.nc", + "description": "Scalar and time-series data produced by ILAMB.", + "dimensions": null + }, + "Reference.nc": { + "filename": "Reference.nc", + "long_name": "Reference.nc", + "description": "Scalar and time-series data produced by ILAMB.", + "dimensions": null + } + }, "plots": { - "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_rmse.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_rmse.png", - "long_name": "The RMSE for ACCESS-ESM1-5-r1i1p1f1-gn over the Tropical climates region.", + "ACCESS-ESM1-5_global_bias.png": { + "filename": "ACCESS-ESM1-5_global_bias.png", + "long_name": "The bias for ACCESS-ESM1-5 over the Global - Land region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -18,13 +37,13 @@ "member_id": "r1i1p1f1", "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", - "region": "tropical", - "statistic": "RMSE" + "region": "global", + "statistic": "Bias" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_mean.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_mean.png", - "long_name": "The period mean for ACCESS-ESM1-5-r1i1p1f1-gn over the Tropical climates region.", + "ACCESS-ESM1-5_global_biasscore.png": { + "filename": "ACCESS-ESM1-5_global_biasscore.png", + "long_name": "The bias score for ACCESS-ESM1-5 over the Global - Land region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -32,13 +51,13 @@ "member_id": "r1i1p1f1", "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", - "region": "tropical", - "statistic": "Period Mean" + "region": "global", + "statistic": "Bias score" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_global_biasscore.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_global_biasscore.png", - "long_name": "The bias score for ACCESS-ESM1-5-r1i1p1f1-gn over the Global - Land region.", + "ACCESS-ESM1-5_global_cycle.png": { + "filename": "ACCESS-ESM1-5_global_cycle.png", + "long_name": "The annual cycle for ACCESS-ESM1-5 over the Global - Land region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -47,12 +66,12 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "global", - "statistic": "Bias score" + "statistic": "Annual cycle" } }, - "None_tropical_taylor.png": { - "filename": "None_tropical_taylor.png", - "long_name": "The Taylor diagram over the Tropical climates region.", + "ACCESS-ESM1-5_global_cyclescore.png": { + "filename": "ACCESS-ESM1-5_global_cyclescore.png", + "long_name": "The annual cycle score for ACCESS-ESM1-5 over the Global - Land region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -60,13 +79,13 @@ "member_id": "r1i1p1f1", "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", - "region": "tropical", - "statistic": "Taylor diagram" + "region": "global", + "statistic": "Annual cycle score" } }, - "None_global_taylor.png": { - "filename": "None_global_taylor.png", - "long_name": "The Taylor diagram over the Global - Land region.", + "ACCESS-ESM1-5_global_mean.png": { + "filename": "ACCESS-ESM1-5_global_mean.png", + "long_name": "The period mean for ACCESS-ESM1-5 over the Global - Land region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -75,32 +94,26 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "global", - "statistic": "Taylor diagram" - } - }, - "Reference_tropical_mean.png": { - "filename": "Reference_tropical_mean.png", - "long_name": "The period mean for the reference data over the Tropical climates region.", - "description": "", - "dimensions": { - "region": "tropical", - "statistic": "Period Mean", - "source_id": "Reference" + "statistic": "Period Mean" } }, - "Reference_global_tmax.png": { - "filename": "Reference_global_tmax.png", - "long_name": "The maxmimum month for the reference data over the Global - Land region.", + "ACCESS-ESM1-5_global_rmse.png": { + "filename": "ACCESS-ESM1-5_global_rmse.png", + "long_name": "The RMSE for ACCESS-ESM1-5 over the Global - Land region.", "description": "", "dimensions": { + "experiment_id": "historical", + "grid_label": "gn", + "member_id": "r1i1p1f1", + "source_id": "ACCESS-ESM1-5", + "reference_source_id": "FLUXNET2015", "region": "global", - "statistic": "Maximum month", - "source_id": "Reference" + "statistic": "RMSE" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_global_bias.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_global_bias.png", - "long_name": "The bias for ACCESS-ESM1-5-r1i1p1f1-gn over the Global - Land region.", + "ACCESS-ESM1-5_global_rmsescore.png": { + "filename": "ACCESS-ESM1-5_global_rmsescore.png", + "long_name": "The RMSE score for ACCESS-ESM1-5 over the Global - Land region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -109,12 +122,12 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "global", - "statistic": "Bias" + "statistic": "RMSE score" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_global_rmsescore.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_global_rmsescore.png", - "long_name": "The RMSE score for ACCESS-ESM1-5-r1i1p1f1-gn over the Global - Land region.", + "ACCESS-ESM1-5_global_shift.png": { + "filename": "ACCESS-ESM1-5_global_shift.png", + "long_name": "The shift in maximum month for ACCESS-ESM1-5 over the Global - Land region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -123,12 +136,12 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "global", - "statistic": "RMSE score" + "statistic": "Shift in maximum month" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_biasscore.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_biasscore.png", - "long_name": "The bias score for ACCESS-ESM1-5-r1i1p1f1-gn over the Tropical climates region.", + "ACCESS-ESM1-5_global_tmax.png": { + "filename": "ACCESS-ESM1-5_global_tmax.png", + "long_name": "The maxmimum month for ACCESS-ESM1-5 over the Global - Land region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -136,13 +149,13 @@ "member_id": "r1i1p1f1", "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", - "region": "tropical", - "statistic": "Bias score" + "region": "global", + "statistic": "Maximum month" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_global_shift.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_global_shift.png", - "long_name": "The shift in maximum month for ACCESS-ESM1-5-r1i1p1f1-gn over the Global - Land region.", + "ACCESS-ESM1-5_global_trace.png": { + "filename": "ACCESS-ESM1-5_global_trace.png", + "long_name": "The regional mean for ACCESS-ESM1-5 over the Global - Land region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -151,12 +164,12 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "global", - "statistic": "Shift in maximum month" + "statistic": "Regional mean" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_shift.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_shift.png", - "long_name": "The shift in maximum month for ACCESS-ESM1-5-r1i1p1f1-gn over the Tropical climates region.", + "ACCESS-ESM1-5_tropical_bias.png": { + "filename": "ACCESS-ESM1-5_tropical_bias.png", + "long_name": "The bias for ACCESS-ESM1-5 over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -165,12 +178,12 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "tropical", - "statistic": "Shift in maximum month" + "statistic": "Bias" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_global_rmse.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_global_rmse.png", - "long_name": "The RMSE for ACCESS-ESM1-5-r1i1p1f1-gn over the Global - Land region.", + "ACCESS-ESM1-5_tropical_biasscore.png": { + "filename": "ACCESS-ESM1-5_tropical_biasscore.png", + "long_name": "The bias score for ACCESS-ESM1-5 over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -178,13 +191,13 @@ "member_id": "r1i1p1f1", "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", - "region": "global", - "statistic": "RMSE" + "region": "tropical", + "statistic": "Bias score" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_trace.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_trace.png", - "long_name": "The regional mean for ACCESS-ESM1-5-r1i1p1f1-gn over the Tropical climates region.", + "ACCESS-ESM1-5_tropical_cycle.png": { + "filename": "ACCESS-ESM1-5_tropical_cycle.png", + "long_name": "The annual cycle for ACCESS-ESM1-5 over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -193,12 +206,12 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "tropical", - "statistic": "Regional mean" + "statistic": "Annual cycle" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_global_cyclescore.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_global_cyclescore.png", - "long_name": "The annual cycle score for ACCESS-ESM1-5-r1i1p1f1-gn over the Global - Land region.", + "ACCESS-ESM1-5_tropical_cyclescore.png": { + "filename": "ACCESS-ESM1-5_tropical_cyclescore.png", + "long_name": "The annual cycle score for ACCESS-ESM1-5 over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -206,13 +219,13 @@ "member_id": "r1i1p1f1", "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", - "region": "global", + "region": "tropical", "statistic": "Annual cycle score" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_global_mean.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_global_mean.png", - "long_name": "The period mean for ACCESS-ESM1-5-r1i1p1f1-gn over the Global - Land region.", + "ACCESS-ESM1-5_tropical_mean.png": { + "filename": "ACCESS-ESM1-5_tropical_mean.png", + "long_name": "The period mean for ACCESS-ESM1-5 over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -220,13 +233,13 @@ "member_id": "r1i1p1f1", "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", - "region": "global", + "region": "tropical", "statistic": "Period Mean" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_global_tmax.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_global_tmax.png", - "long_name": "The maxmimum month for ACCESS-ESM1-5-r1i1p1f1-gn over the Global - Land region.", + "ACCESS-ESM1-5_tropical_rmse.png": { + "filename": "ACCESS-ESM1-5_tropical_rmse.png", + "long_name": "The RMSE for ACCESS-ESM1-5 over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -234,23 +247,13 @@ "member_id": "r1i1p1f1", "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", - "region": "global", - "statistic": "Maximum month" - } - }, - "Reference_global_mean.png": { - "filename": "Reference_global_mean.png", - "long_name": "The period mean for the reference data over the Global - Land region.", - "description": "", - "dimensions": { - "region": "global", - "statistic": "Period Mean", - "source_id": "Reference" + "region": "tropical", + "statistic": "RMSE" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_cycle.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_cycle.png", - "long_name": "The annual cycle for ACCESS-ESM1-5-r1i1p1f1-gn over the Tropical climates region.", + "ACCESS-ESM1-5_tropical_rmsescore.png": { + "filename": "ACCESS-ESM1-5_tropical_rmsescore.png", + "long_name": "The RMSE score for ACCESS-ESM1-5 over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -259,12 +262,12 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "tropical", - "statistic": "Annual cycle" + "statistic": "RMSE score" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_rmsescore.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_rmsescore.png", - "long_name": "The RMSE score for ACCESS-ESM1-5-r1i1p1f1-gn over the Tropical climates region.", + "ACCESS-ESM1-5_tropical_shift.png": { + "filename": "ACCESS-ESM1-5_tropical_shift.png", + "long_name": "The shift in maximum month for ACCESS-ESM1-5 over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -273,12 +276,12 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "tropical", - "statistic": "RMSE score" + "statistic": "Shift in maximum month" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_bias.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_bias.png", - "long_name": "The bias for ACCESS-ESM1-5-r1i1p1f1-gn over the Tropical climates region.", + "ACCESS-ESM1-5_tropical_tmax.png": { + "filename": "ACCESS-ESM1-5_tropical_tmax.png", + "long_name": "The maxmimum month for ACCESS-ESM1-5 over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -287,12 +290,12 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "tropical", - "statistic": "Bias" + "statistic": "Maximum month" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_global_trace.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_global_trace.png", - "long_name": "The regional mean for ACCESS-ESM1-5-r1i1p1f1-gn over the Global - Land region.", + "ACCESS-ESM1-5_tropical_trace.png": { + "filename": "ACCESS-ESM1-5_tropical_trace.png", + "long_name": "The regional mean for ACCESS-ESM1-5 over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -300,23 +303,13 @@ "member_id": "r1i1p1f1", "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", - "region": "global", - "statistic": "Regional mean" - } - }, - "Reference_tropical_tmax.png": { - "filename": "Reference_tropical_tmax.png", - "long_name": "The maxmimum month for the reference data over the Tropical climates region.", - "description": "", - "dimensions": { "region": "tropical", - "statistic": "Maximum month", - "source_id": "Reference" + "statistic": "Regional mean" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_global_cycle.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_global_cycle.png", - "long_name": "The annual cycle for ACCESS-ESM1-5-r1i1p1f1-gn over the Global - Land region.", + "None_global_taylor.png": { + "filename": "None_global_taylor.png", + "long_name": "The Taylor diagram over the Global - Land region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -325,12 +318,12 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "global", - "statistic": "Annual cycle" + "statistic": "Taylor diagram" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_cyclescore.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_cyclescore.png", - "long_name": "The annual cycle score for ACCESS-ESM1-5-r1i1p1f1-gn over the Tropical climates region.", + "None_tropical_taylor.png": { + "filename": "None_tropical_taylor.png", + "long_name": "The Taylor diagram over the Tropical climates region.", "description": "", "dimensions": { "experiment_id": "historical", @@ -339,21 +332,51 @@ "source_id": "ACCESS-ESM1-5", "reference_source_id": "FLUXNET2015", "region": "tropical", - "statistic": "Annual cycle score" + "statistic": "Taylor diagram" } }, - "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_tmax.png": { - "filename": "ACCESS-ESM1-5-r1i1p1f1-gn_tropical_tmax.png", - "long_name": "The maxmimum month 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backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/e2a5786432e4ca6860602bcee6a982137da5e166/series.json rename to backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/historical_gn_r1i1p1f1_ACCESS-ESM1-5_g36_v1_dcfcc801/19/series.json index a52fd6c..7e0d00d 100644 --- a/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/e2a5786432e4ca6860602bcee6a982137da5e166/series.json +++ b/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/results/ilamb/gpp-fluxnet2015/historical_gn_r1i1p1f1_ACCESS-ESM1-5_g36_v1_dcfcc801/19/series.json @@ -1,14 +1,300 @@ [ { + "attributes": { + "long_name": "Carbon Mass Flux out of Atmosphere Due to Gross Primary Production on Land", + "standard_name": "gross_primary_productivity_of_biomass_expressed_as_carbon", + "units": "g d-1 m-2" + }, "dimensions": { - "metric": 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