Observation List – Django REST framework

Get a list of Observation objects.

### Available end points:

- `/observations/` - Will list all Results in the database
- `/observations.json` - Will return all Results in json format. This can
also be achieved by using the accept header. `application/json`
- `/observations/<object_id>/` - Returns Results object with that id

### Available Methods:

- `GET`
- `HEAD`

### Available filters:

- `title`
- `uuid`
- `keywords`
- `status`
- `result_field`
- `discoveryKeywords`
- `updateFrequency`
- `nonGeographicFlag`
- `publicationState`
- `permissions`

### How to use filters:

These filters can be used like django query filters using __ for related model relationships.

- `/observations/?uuid=d594d53df2612bbd89c2e0e770b5c1a0`
- `/observations/?status=completed`
- `/observations/?results_field__dataPath__startswith=/neodc/esacci`
- `/observations/?discoveryKeywords__name=ESACCI`
- `/observations/?nonGeographicFlag=True`

Filters can be stacked to build an 'AND' relationship. E.g.

- `/observations/?publicationState__in=published,citable&nonGeographicFlag=True`
- `/observations/?publicationState__in=published,citable&discoveryKeyword__name=NDGO0003`

GET /api/v2/observations/?format=api&offset=8200

HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "count": 10253,
    "next": "https://api.catalogue.ceda.ac.uk/api/v2/observations/?format=api&limit=100&offset=8300",
    "previous": "https://api.catalogue.ceda.ac.uk/api/v2/observations/?format=api&limit=100&offset=8100",
    "results": [
        {
            "ob_id": 36953,
            "uuid": "65fe153565994b8d8f4714003d90117c",
            "title": "FAAM C253 ACRUISE-2 flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
            "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for ACRUISE : Atmospheric Composition and Radiative forcing changes due to  UN International Ship Emissions regulations (ACRUISE) (ACRUISE-2) project.",
            "keywords": "ACRUISE-2, FAAM, airborne, atmospheric measurments",
            "publicationState": "published",
            "dataPublishedTime": "2022-02-22T22:03:22",
            "doiPublishedTime": null,
            "updateFrequency": "asNeeded",
            "status": "ongoing",
            "result_field": {
                "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/36952/?format=api",
                "dataPath": "/badc/faam/data/2021/c253-sep-27",
                "oldDataPath": [],
                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 2560207198,
                "numberOfFiles": 45,
                "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted."
            },
            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/10209/?format=api",
            "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/3394/?format=api",
            "nonGeographicFlag": false,
            "phenomena": [
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3220/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3221/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3222/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3223/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3224/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3225/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3226/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3227/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3228/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3229/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3230/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3231/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3232/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3233/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3234/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3235/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3236/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3237/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3238/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3239/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3240/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3241/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3242/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3243/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3244/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3245/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3246/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3247/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3248/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3249/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3250/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3251/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3252/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3253/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3254/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3255/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3256/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3257/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3258/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3259/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3260/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3261/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3262/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3263/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3264/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3265/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3266/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3267/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3268/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3269/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3270/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3271/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3272/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3273/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3274/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3275/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3276/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3277/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3278/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3279/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3280/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3281/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3282/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3283/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3284/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3285/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3286/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3287/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3288/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3289/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3290/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3291/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3292/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3295/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3307/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3315/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3316/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3317/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3318/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3319/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3320/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3321/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3322/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3323/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3324/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3325/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3326/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3327/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3328/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3329/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3330/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3331/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3332/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3333/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3334/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3335/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3336/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3337/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3338/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3339/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3340/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3341/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3342/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3343/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3344/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/3345/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14845/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14846/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14848/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14850/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14851/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14852/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14853/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14854/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14855/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14857/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14924/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14927/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14928/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14935/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/14946/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15040/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15041/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15096/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15282/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15283/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15325/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15338/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15342/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15343/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15813/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15818/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15819/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15820/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15822/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15823/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15836/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/15845/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/16569/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/16658/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/20689/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/20695/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/20696/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/20697/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/21049/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/22364/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/22462/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/22465/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/22472/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/22847/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/23620/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/24825/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/25743/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/25744/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/25745/?format=api",
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                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47310/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47311/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47312/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47313/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47314/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47315/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47316/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47317/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47318/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47319/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47320/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47321/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47323/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47324/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47325/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47326/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47327/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47328/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47329/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47330/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47331/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47332/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/47333/?format=api",
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        {
            "ob_id": 37066,
            "uuid": "a07ea49c7b754f15af99005cd351f550",
            "title": "Passive seismicity recorded using four-component ocean bottom seismometers deployed in and around an active fluid flow structure: Scanner Pockmark, North Sea",
            "abstract": "A passive source seismic dataset was acquired during RRS James Cook cruise JC152, around the Scanner Pockmark Complex in the North Sea. Data were recorded on 25 four-component ocean bottom seismometers (OBS - hydrophone and three-component geophone), deployed in and around Scanner Pockmark, recording at a sampling rate of 4 kHz. The OBS recorded continuously between deployment and recovery (28/08/2017 to 05/09/2017) including through periods of active source seismic data acquisition, indicated in the cruise report (Bull, 2017).\r\n\r\nData are provided in miniSEED format. The receiver drop locations are provided in the associated metadata directory. The data were acquired as part of the 'Characterization of major overburden leakage pathways above sub-seafloor CO2 storage reservoirs in the North Sea' (CHIMNEY) project, funded by the Natural Environment Research Council (NERC) under grant reference NE/N016130/1.",
            "keywords": "seismic reflection, ocean bottom seismograph, active source seismology, passive source seismology, fluid flow pathways",
            "publicationState": "published",
            "dataPublishedTime": "2022-04-26T16:05:20",
            "doiPublishedTime": null,
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            "dataLineage": "Passive seismic data were acquired during NERC cruise JC152 from August-September 2017 in the North Sea. The raw data were processed by the science party and provided to the British Oceanographic Data Centre for long-term archive. Data were subsequently archived at CEDA.",
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            "ob_id": 37072,
            "uuid": "96dc6996b5534052ab44f3098a99a357",
            "title": "Multi-frequency, multi-channel seismic dataset acquired around an active fluid flow structure: Scanner Pockmark, North Sea.",
            "abstract": "A multi-channel seismic dataset was acquired during RRS James Cook cruise JC152 (August - September 2017), around the Scanner Pockmark Complex in the North Sea. Data were recorded using a number of different seismic sources, comprising: \r\n\r\n1) a GI airgun array, used in two different configurations for separate parts of the survey, \r\ni) a 420 ci (2 x 105/105 ci) array operated in harmonic mode and fired at 8 s intervals, and \r\nii) a 300 ci (2 x 45/105 ci) array operated in true GI mode and fired at 6 s intervals, both towed at 2 m depth below sea surface; \r\n\r\n2) an Applied Acoustic Engineering Squid sparker (1750 or 2000 J), towed at the sea surface and triggered at 2 s intervals; and, \r\n\r\n3) a Duraspark sparker (2000 J), towed at the sea surface and triggered at 2 s intervals. Signals produced by the GI airguns and surface sparkers were recorded on two towed multi-channel streamers: \r\na) a 60 channel, 1 m group interval streamer recorded on a Geometrics Strataview R60 recording system, and \r\nb), a 120 channel, 1.56 m group interval GeoEel streamer, at sampling rates between 0.125 and 0.5 ms depending on the streamer and source pairing. \r\n\r\nData are provided in standard SEG-D format. The data were acquired as part of the 'Characterization of major overburden leakage pathways above sub-seafloor CO2 storage reservoirs in the North Sea' (CHIMNEY) project, funded by the Natural Environment Research Council (NERC) under grant reference NE/N016130/1.",
            "keywords": "seismic reflection, ocean bottom seismograph, active source seismology, passive source seismology, fluid flow pathways",
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            "ob_id": 37090,
            "uuid": "540a4c4cdfa6497993bbfa7c3e3df51a",
            "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): Reference state data",
            "abstract": "Reference state data derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis for the nudging experiments of the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. \r\nThese reference states are used to nudge the stratosphere towards a specified evolution in the ensemble forecasts carried out by the SNAPSI project.\r\nThe data contain: \r\n(a) lightly processed horizontal winds and temperatures from ERA5 spanning three case studies of sudden stratospheric warmings from 2018 to 2019 and \r\n(b) climatological horizontal winds and temperatures.",
            "keywords": "stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC",
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            "dataPublishedTime": "2022-04-19T16:51:10",
            "doiPublishedTime": "2022-04-20T16:09:36",
            "updateFrequency": "",
            "status": "completed",
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                "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/SNAP/SNAPSI-REF",
                "oldDataPath": [],
                "storageLocation": "internal",
                "storageStatus": "online",
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            "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nThe data is based on model-level output from ERA5, downloaded on a 1x1 degree horizontal grid. It has been lightly processed to compute the climatology and put in a format consistent with the SNAPSI data request.",
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            "language": "English",
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177285/?format=api",
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            "uuid": "03c935c6890c4b2ebf4aae4d84cd9472",
            "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 2.0.1",
            "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.0.1 of the dataset.   The five thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website.",
            "keywords": "ESA, CCI, Lakes, ECV",
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                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84557/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84558/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84568/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84569/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84570/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84571/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84572/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84573/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84574/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84575/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84576/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84577/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84578/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84579/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84580/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84581/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84582/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84583/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84584/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84585/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84586/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84587/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84588/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84589/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84590/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84591/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84592/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84593/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84594/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84595/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84596/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84597/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84598/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84599/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84600/?format=api"
            ],
            "dataLineage": "This dataset was generated in the framework of the Lakes CCI+ project, funded by ESA. Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\n\r\nV2.0.1 of the data provides a minor update to v2.0, which fixes an issue with missing latitude and longitude values in some files, and minor metadata and format corrections.  The data itself is unchanged.",
            "removedDataTime": null,
            "removedDataReason": "",
            "language": "English",
            "identifier_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/12412/?format=api"
            ],
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                "https://api.catalogue.ceda.ac.uk/api/v2/projects/30309/?format=api"
            ],
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            "responsiblepartyinfo_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177293/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177294/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177295/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177296/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177297/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177298/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177299/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177300/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177301/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177302/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177303/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177304/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177305/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177306/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177307/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177308/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177309/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177310/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177312/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177311/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177313/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177314/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177315/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177316/?format=api"
            ],
            "procedureAcquisition": null,
            "procedureCompositeProcess": null,
            "procedureComputation": "https://api.catalogue.ceda.ac.uk/api/v2/computations/32839/?format=api",
            "permissions": [
                {
                    "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/2551/?format=api",
                    "useLimitation": null,
                    "accessConstraints": null,
                    "accessCategory": "public",
                    "accessRoles": null,
                    "label": "public: None group",
                    "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lakes_terms_and_conditions_v2.pdf",
                    "licenceClassifications": "any"
                }
            ],
            "discoveryKeywords": [
                {
                    "ob_id": 1138,
                    "name": "NDGO0003"
                }
            ]
        },
        {
            "ob_id": 37093,
            "uuid": "10ff77c6b52143d987ab1f4a46834f5c",
            "title": "CRU TS3.23_clim: 30 year climatologies derived from Climatic Research Unit (CRU) Time-Series (TS) Version 3.23 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2014)",
            "abstract": "These data are 30 year climatologies produced from the gridded CRU TS (time-series) 3.23 data are month-by-month variations in climate over the period 1901-2014, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia. 4 sets of climatologies are produced covering the periods: 1901-1930, 1931-1960, 1961 - 1990 and 1984-2013.\r\n\r\nCRU TS 3.23 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period Jan. 1901 - Dec. 2014.\r\n\r\nCRU TS 3.23 data were produced using the same methodology as for the 3.21 datasets. In addition to updating the dataset with 2014 data, some new stations have been added for TMP and PRE only. Known issues predating this release remain; the 4.00 release, due soon, will address these.\r\n\r\nThe 4.00 release will utilise Angular-Distance Weighting (ADW) gridding, promising more accurate results with far greater adjustability and logging. It will cover the same spatial, temporal and variate spaces as version 3.23 (land areas excluding Antarctica at 0.5°x0.5°, monthly from 1901 to 2014 with no missing values, 10 variables).\r\n\r\nVersions 3.23 and 4.00 will run concurrently until 2016, after which the new (ADW) approach will be used. This is to allow comparisons between the methods and results to be made by users of the dataset.\r\n\r\nThe CRU TS 3.23 data are monthly gridded fields based on monthly observational data, which are calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and netcdf data files both contain monthly mean values for the various parameters.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies.\r\n\r\nCRU TS data are available for download to all CEDA users. The CEDA Web Processing Service (WPS) may be used to extract a subset of the data (please see link to WPS below).",
            "keywords": "CRU TS, ATMOSPHERE, EARTHSCIENCE",
            "publicationState": "preview",
            "dataPublishedTime": null,
            "doiPublishedTime": null,
            "updateFrequency": "notPlanned",
            "status": "completed",
            "result_field": {
                "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/37149/?format=api",
                "dataPath": "/badc/cru/data/cru_ts/cru_ts_3.23_clim",
                "oldDataPath": [],
                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 995759026,
                "numberOfFiles": 41,
                "fileFormat": "Data are netCDF formatted."
            },
            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/10266/?format=api",
            "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/513/?format=api",
            "nonGeographicFlag": false,
            "phenomena": [
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/50559/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/50561/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/56250/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/56251/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/56252/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/56253/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/56254/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/56255/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/56256/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/56257/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/56258/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/60438/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/61269/?format=api"
            ],
            "dataLineage": "The exact origins of these data are unclear. However, it would appear that they are 30 year climatologies derived from the source TS 3.23 data by a member of the BADC team around 2015-2016 when these were added to the CEDA Archive.\r\n\r\nCRU TS 3.00 data files acquired directly from CRU in 2007. CRU provided the BADC with software to generate the CRU datasets in 2010, and this was used to produce CRU TS 3.10 at the BADC in early 2011.\r\n\r\nIn July 2012, systematic errors were discovered in the CRUTS v3.10 process. The effect was, in some cases, to reduce the gridded values for PRE and therefore WET. Values of FRS were found to be unrealistic in some areas due to the algorithms used for synthetic generation. The files (pre, frs and wet) were immediately removed from BADC. The corrected run for precipitation, based on the v3.10 precipitation station data, was generated as a direct replacement and given the version number 3.10.01. There were no corrected runs produced for wet and frs.\r\n\r\nCRU TS 3.20 was produced in December 2012.\r\nIn March 2013, CRU TS observation databases for TMP and PRE variables were provided by CRU. Others are in preparation. In july 2013, two errors were found in the PRE and WET variables of CRU TS v3.20. These have been repaired in CRU TS v3.21. Details of the errors found are available in the Release Notes in the archive.\r\n\r\nCRU TS 3.21 was produced in July 2013. \r\n\r\nCRU TS 3.22 was produced in July 2014.\r\n\r\nIn October 2015, CRU TS 3.23 provided by CRU, is the latest version available, superseding all previous data versions (which are available to allow user comparisons).",
            "removedDataTime": null,
            "removedDataReason": "",
            "language": "English",
            "identifier_set": [],
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                "https://api.catalogue.ceda.ac.uk/api/v2/projects/6672/?format=api"
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177317/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177318/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177319/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177320/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177321/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177322/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177323/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177324/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177325/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177326/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177327/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177328/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177329/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177330/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177543/?format=api"
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            "procedureAcquisition": null,
            "procedureCompositeProcess": null,
            "procedureComputation": "https://api.catalogue.ceda.ac.uk/api/v2/computations/6676/?format=api",
            "permissions": [
                {
                    "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/2522/?format=api",
                    "useLimitation": null,
                    "accessConstraints": null,
                    "accessCategory": "registered",
                    "accessRoles": null,
                    "label": "registered: None group",
                    "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/",
                    "licenceClassifications": "any"
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            "discoveryKeywords": []
        },
        {
            "ob_id": 37111,
            "uuid": "c6f1b1ff16f8407386e2d643bc5b916a",
            "title": "CANDIFLOS : Surface fluxes from ACSE measurement campaign on icebreaker Oden, 2014",
            "abstract": "Characterising and Interpreting FLuxes Over Sea Ice (CANDIFLOS) is a data analysis project drawing upon data from multiple field campaigns. It aims to improve the parameterization of surface fluxes over sea ice. This data set consists of the processed surface heat fluxes and sea ice fractions from the Arctic Clouds Summer Experiment (ACSE) project (2014) conducted on icebreaker Oden. Matching data from the AO2016 cruise are provided as a separate data set.",
            "keywords": "turbulent fluxes, meteorology, winds, wind speed",
            "publicationState": "citable",
            "dataPublishedTime": "2022-04-05T11:06:37",
            "doiPublishedTime": "2022-04-05T11:06:55.990154",
            "updateFrequency": "notPlanned",
            "status": "completed",
            "result_field": {
                "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/37112/?format=api",
                "dataPath": "/badc/deposited2022/candiflos/data/oden_surface_flux2014",
                "oldDataPath": [],
                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 1596390,
                "numberOfFiles": 3,
                "fileFormat": "Data are NetCDF formatted."
            },
            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/10258/?format=api",
            "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/3433/?format=api",
            "nonGeographicFlag": false,
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                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63685/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63686/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63687/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63688/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63689/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63690/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63691/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63692/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63693/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63694/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63695/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63696/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63697/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63698/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63699/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63700/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63701/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63702/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63703/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63704/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63705/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63706/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63707/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63708/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63709/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63710/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63711/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63712/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63713/?format=api"
            ],
            "dataLineage": "Data were collected by University of Leeds and University of Stockholm and deposited at the Centre for Environmental Data Analysis (CEDA) for archiving.",
            "removedDataTime": null,
            "removedDataReason": "",
            "language": "English",
            "identifier_set": [
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                "https://api.catalogue.ceda.ac.uk/api/v2/projects/37113/?format=api"
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177389/?format=api",
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177392/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177393/?format=api",
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            "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2018), version 3.00",
            "abstract": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nThe monthly dataset starts from March 2000 and ends  December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.",
            "keywords": "land surface temperature, ESA, CCI",
            "publicationState": "citable",
            "dataPublishedTime": "2022-06-27T15:08:50",
            "doiPublishedTime": "2022-06-28T11:10:39",
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            ],
            "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). \r\nContact: data.lst-cci@acri-st.fr",
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        {
            "ob_id": 37126,
            "uuid": "785ef9d3965442669bff899540747e28",
            "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00",
            "abstract": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.\r\n\r\nThe dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml).\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. \r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.",
            "keywords": "land surface temperature, ESA, CCI, infra-red",
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            "doiPublishedTime": "2022-06-28T11:08:52",
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                "volume": 853935291448,
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            ],
            "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr",
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        {
            "ob_id": 37137,
            "uuid": "53a1acaf5adb4b22b19397bf08d229ef",
            "title": "Transition Air: Street side measurements of dispersed NO from an NO bottle in Oxford.",
            "abstract": "This dataset contains measurements of dispersed Nitric Oxide (NO) made at a relatively low traffic area, Mansfield Road, in the city of Oxford, U.K.  as part of project funded through the TRANSITION AIR QUALITY Network.   NO was released from a bottle with a known concentration of 10,000ppm NO in nitrogen mounted at the back of an electric van and the dispersed NO measured at the roadside using fast response sensors provided by Cambustion Ltd. These measurements were then used to validate Computational Fluid Dynamics Models. The tests were carried out during a single day at different locations on the road. Wind direction, wind speed and the concentration of sensed NO are recorded and presented in the archived dataset.",
            "keywords": "Transition Air Quality, NO, Nitric oxide",
            "publicationState": "citable",
            "dataPublishedTime": "2022-04-06T09:25:15",
            "doiPublishedTime": "2022-04-06T10:27:49.610054",
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            "status": "ongoing",
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                "dataPath": "/badc/deposited2022/Oxford_MansfieldRd_roadside_NO",
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                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 1772688612,
                "numberOfFiles": 33,
                "fileFormat": "Data are CSV formatted with an explanatory text file (Guide_for_DATA.txt)."
            },
            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/10262/?format=api",
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            "dataLineage": "The measurements were performed by the hired sensing machines of Cambustion and transferred to the host institute (Oxford Brookes)via mail and emails. Then organised and analysed and uploaded to CEDA.",
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        {
            "ob_id": 37150,
            "uuid": "a3231e64ffea42e7ab9ff0cf72302050",
            "title": "SWIGS: Gorgon Magnetohydrodyamic Code Simulation Data: Magnetospheric and Ionospheric Conditions during Sudden Commencement",
            "abstract": "This dataset contains outputs generated using the Gorgon Magnetohydrodynamic (MHD) code, for simulations of the magnetosphere-ionosphere system during impact by a series of interplanetary shocks with different solar wind conditions and dipole magnetic field orientations. \r\n\r\nThe MHD equations were solved in the magnetosphere on a regular 3-D cartesian grid of resolution 0.25 RE (Earth radii), covering a domain of dimensions (-30,90) RE in X, (-40,40) RE in Y and (-40,40) RE in Z with an inner boundary at 3 RE. In this coordinate system the Sun lies in the negative X-direction, the Z axis is aligned to the dipole in the 0 degree tilt case (where positive tilt points the north magnetic pole towards the Sun), and Y completes the right-handed set. The ionospheric variables were calculated on a separate 2-D spherical grid of dimensions 128x256 in latitude and longitude (with the north pole at 90 degrees latitude and the Sun at 180 degrees longitude), coupled to the magnetospheric domain at the inner boundary. \r\n\r\n5 different shocks were simulated in total, with the following solar wind jump conditions injected at the sunward edge at 7200s simulation time:\r\n\r\nShocks 1-4: n = 5 /cm^3 -> 10 /cm^3 (number density)         \r\n            v = 400 km/s -> 600 km/s (velocity)        \r\n            T = 5 eV -> 417 eV (temperature)     \r\n            B = 2 nT -> 4 nT (interplanetary magnetic field) \r\n\r\nShock 5:    n = 5 /cm^3 -> 20 /cm^3 (number density)    \r\n            v = 400 km/s -> 1000 km/s (velocity)\r\n            T = 5 eV -> 1250 eV (temperature)  \r\n            B = 2 nT -> 4 nT (interplanetary magnetic field)      \r\n\r\nShocks 1, 3 and 5 had an interplanetary magnetic field (IMF) clock angle of 180 degrees, i.e. B = Bz = -2 nT, whereas Shocks 2 and 3 had IMF clock angles of 135 degrees and 90 degrees, respectively. In addition, Shocks 1, 2, 3 and 5 had zero dipole tilt, whereas Shock 4 had a tilt angle of 30 degrees. These simulations employed zero electrical resistivity. The simulations of Shocks 1, 3 and 5 were then repeated utilising an explicit resistivity eta with value of eta/mu_0 = 5e10 m^2/s. The full set of 8 simulations are labelled 'Shock1', 'Shock2', 'Shock3', 'Shock4', 'Shock5' for the zero resistivity runs and 'Shock1_res', 'Shock3_res' and 'Shock5_res' for those with explicit resistivity. \r\n\r\nOutput grid data are timestamped in seconds and are defined at the centre of the grid cells, stored as .hdf5 files for each timestep. Output time-series data are for a single variable over a simulation time range, stored in .csv files. The simulation data corresponding to each shock are stored in separate directories, according to the simulation labels listed above.\r\n\r\nThe magnetospheric variables are stored in the files 'Gorgon_[YYYYMMDD]_[RUN]_MS_params_[XXXX]s.hdf5' where RUN is the simulation label and XXXX is the simulation time in seconds. The magnetospheric data are in SI units and include the magnetic field vector ('Bvec_c'), electric current density vector ('jvec') and ion thermal pressure ('P') for multiple timesteps following initialisation at 7200s of simulation time. The dataset for each magnetospheric variable is of shape (480,320,320,3) for vectors and (480,320,320) for scalars, where the first 3 dimensions are the grid indices in (X,Y,Z) indexed from negative to positive, and the final dimension is the cartesian vector components in (i,j,k).\r\n\r\nSimilarly, the ionospheric data are stored as 'Gorgon_[YYYYMMDD]_[RUN]_IS_params_[XXXX]s.hdf5', containing the field-aligned current density ('FAC') in SI units for multiple timesteps following initialisation at 7200s of simulation time. The dataset for each ionospheric variable is of shape (130, 256) where the first dimension is the grid index in colatitude, indexed from the north towards the south (i.e. 0 to 180 degrees), and the second dimension is the grid index in longitude, indexed from midnight towards noon via dawn (i.e. 0 to 360 degrees).\r\n\r\nFinally, the time-series data are stored as 'Gorgon_[YYYYMMDD]_[RUN]_[XXX].csv' where 'X' is the simulation label and 'XXX' is the time-series variable. These include the subsolar magnetopause standoff distance 'RMP' in RE, and the North (and South) polar cap flux content 'FPC' in Wb*RE^2; in each case the first column contains the simulation time in seconds, with the variables in the second (and third) column(s).   NE/P017142/1",
            "keywords": "SWIGS",
            "publicationState": "citable",
            "dataPublishedTime": "2022-04-11T10:49:17",
            "doiPublishedTime": "2022-04-11T10:50:52.301531",
            "updateFrequency": "notPlanned",
            "status": "completed",
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            "title": "HadUK-Grid Climate Observations by UK river basins, v1.1.0.0 (1836-2021)",
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            "title": "IPCC Data Distribution Centre subsets provided by the LINK project.",
            "abstract": "This dataset contains output data from a number of models  associated  with the IPCC Third Assessment Report.  This data was processed at the Climate Research Unit at the University of East Anglia. The data extraction was intended for use by the Climate Impacts Community (and was funded by the UK Department of Environment Food and Rural Affairs, Defra).\r\n\r\nData from various modelling centres and models: CCCMA, CSIRO, ECHAM4, GFDL99, HADCM3, NIES99.",
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            "dataLineage": "Data from various modelling centres and models: CCCMA, CSIRO, ECHAM4, GFDL99, HADCM3, NIES99.  Data extracted by CRU and transfered to the BADC.",
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            "ob_id": 37273,
            "uuid": "7fbb95c288af44ab8b40e74fef0e7cbc",
            "title": "Velocity and strain rate fields of the northeast Tibetan Plateau",
            "abstract": "This data set contains velocity and strain rate fields over the northeast Tibetan Plateau, which are derived from Sentinel-1A and -1B synthetic aperture radar satellite data (SAR) and stored in GeoTIFF (.tif) or NETCDF (.grd) formats.\r\nThe velocities in the line-of-sights (LOS) of the satellites were processed at ~100 m resolution from time series in ~250km x 250km frames. The data set consists of velocities from 10 frames in ascending tracks and 13 frames in descending tracks of the satellites' orbits. The spatial extent of the velocities spans 96E-108E and 32N-43N, covering an area of 660,000 km^2. The temporal coverages of the data span from October 2014 to December 2019 across 65-110 acquisition epochs. The uncertainties of the velocities average to <1 mm/yr. The time series are inverted from fully-connected networks of short-temporal-baseline interferograms which are generated from interfering and unwrapping pairs of SAR imagery. The velocities represent the average velocity through the displacement time series. \r\n\r\nThe LOS velocities were decomposed into east and vertical velocities which are also archived with associated uncertainties. These Cartesian fields cover the overlapping areas between ascending and descending tracks and total 440,000 km^2. By combining the horizontal gradients of the filtered east velocities and interpolated north velocities from Global Navigational Satellite System, we derive second invariant, maximum shear, and dilatation strain rate fields for the same area with 1 km sampling intervals. \r\nThese strain rate fields highlight creeping sections and strain concentration on faults and fault junctions. The velocity fields reveal fault kinematics in terms of slip rates and partitioning. The vertical velocities also show non-tectonic signals such as subsidence related to permafrost melting, groundwater extraction, and reservoir loading, as well uplift from blocked drainages. \r\n\r\nThe data are collected and processed by Qi Ou with the automatic processing tools developed by Milan Lazecky. Velocity and strain rate fields were interpreted by all authors. By default, interferograms were generated from each epoch to six consecutive epochs and between acquisition pairs with six-month and nine-month temporal baselines. Interferograms with the unwrapping error were removed from the network and all networks were continuous and fully connected.",
            "keywords": "COMET, Sentinel 1",
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            "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). \r\nThe original interferograms are available on COMET-LiCS portal (https://comet.nerc.ac.uk/comet-lics-portal/) The interferograms are processed from Sentinel-1 Level 1 (L1) Synthetic Aperture Radar (SAR) imagery acquired by the European Space Agency (https://scihub.copernicus.eu/dhus/#/home) using the Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) routine. The average line-of-sight (LOS) velocities and associated uncertainties are derived from frame-based five-year time series, which are inverted from networks of short temporal baseline interferograms using the New Small Baseline Subset (NSBAS) method. The scaled uncertainties are the LOS uncertainties with referencing effects corrected by fitting a spherical model to the scatter points between uncertainty and distance from the reference. The stitched LOS velocities in the reference frame of the Global Navigational Satellite System (GNSS) velocities are the results of mosaicking frame-sized LOS velocities into tracks by adding a planar ramp per frame to close the differences between overlapping pixels in consecutive LOS frames and between InSAR and GNSS LOS velocities. The stitched LOS velocities in two line-of-sights were then decomposed into Cartesian velocities in two steps, first into an east component and a combination of the north and vertical components, and then resolving the vertical component from the combination component using an interpolated north component from the GNSS velocities. The strain rate fields are calculated from the horizontal gradients of the filtered InSAR east velocities and interpolated GNSS north velocities.",
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        {
            "ob_id": 37276,
            "uuid": "48cd535e93574c8da8e80b91e06c7d51",
            "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v2.2",
            "abstract": "This dataset provides a Gravimetric Mass Balance (GMB) product for the Greenland Ice Sheet (GIS), generated by DTU Space, based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through August 2021.\r\n\r\nThe GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 200 monthly solutions. The mass change estimation is based on inversion method developed at DTU Space.\r\n\r\nTwo different types of products are available. First, the  gridded mass trends product is comprised of ice mass change trends for cells of equal area with 50 km resolution covering the whole GIS. Second, the mass change time series product provides time series of integrated mass changes for 8 drainage basins and the entire GIS.\r\n\r\nReference:\r\nBarletta, V. R., Sørensen, L. S., and Forsberg, R. (2013) 'Scatter of mass changes estimates at basin scale for Greenland and Antarctica', The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013.\",",
            "keywords": "Greenland Ice Sheet, CCI, ESA, Gravimetric Mass Balance",
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            "dataPublishedTime": "2025-04-03T14:50:57",
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                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/80395/?format=api"
            ],
            "dataLineage": "Data have been produced by DTU Space within the ESA Greenland Ice Sheet CCI project, and the original version is stored at data.dtu.dk  https://doi.org/10.11583/DTU.12866579\r\n\r\nA copy of the data has been supplied to the Centre for Environmental Data Analysis (CEDA) for the CCI Open Data Portal.",
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        {
            "ob_id": 37280,
            "uuid": "f3515388768344bfb2be0521f82388be",
            "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.11 (v20220510)",
            "abstract": "Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 includes mapped and time-series data showing global surface temperature relative to 1850 - 1900 over multiple time scales.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\n Figure has three panels, with data provided for panel (a) (center and right part), and panel (c).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n Global surface temperature, relative to 1850 - 1900 for:\r\n\r\n Panel a: \r\n \r\n - 1000 to 1900 CE - from PAGES 2k Consortium (modified from the version 2019: 10.1038/s41561-019-0400-0)\r\n - 1850 to 2020 from AR6 assessed mean (same as Figure 2.11c).\r\n\r\n Panel c: \r\n \r\n - Annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\n Panel a:\r\n \r\n - Data file: Figure_2_11a-PAGES_2k_Consortium.csv (yearly data, 1000 to 1900); relates to the center part of the figure showing global surface temperature relative to 1850 -1900. (bold solid green line, column 2, median 10-yr smooth adjusted (+0.37°C), thin solid green lines: 5th (column 3) and 95th (column 4) percentiles of the ensemble members).\r\n - Data file: Figure2_11_panel_a.csv (yearly data, 1850 to 2020); relates to the right part of the figure showing global temperature anomaly AR6 assessed mean. (bold solid violet line, column 2)\r\n\r\nPanel c: \r\n \r\n - Data file: Figure_2_11c-land_and_ocean_time_series.csv (yearly data, 1850 to 2020); relates to the upper part of the figure showing global surface temperature relative to 1850 -1900. (Land, column 2, red line; Ocean, column 3, blue line).\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nInput data and code to reproduce panel b and panel c (lower part) plots are provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to input data figure 2.11.\r\n - Link to the code for the figure, archived on Zenodo.",
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            "title": "HadISDH.blend: gridded global monthly land and ocean surface humidity data version 1.3.0.2021f",
            "abstract": "This is the HadISDH.blend 1.3.0.2021f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2021.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2021. It combines the latest version of HadISDH.land and HadISDH.marine. and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013.",
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            "title": "Simulation data used in the Suppression of surface ozone by an aerosol-inhibited photochemical ozone regime journal article",
            "abstract": "This dataset contains the data used to plot results found in the Suppression of surface ozone by an aerosol-inhibited photochemical ozone regime journal article published in Nature Geoscience. The simulations were run using the GEOS-Chem V12.8 chemical transport model at 0.5-degree horizontal resolution over the domain 170W-170E, 10S-60N using 2017 meteorological data for 1750, 1970 and 2014 emissions scenarios.   July 2017 GEOS-FP (forward-processing) meteorological fields were used for all simulations.  \r\n\r\nThree experimental runs were performed using 1750 emissions; no sea salt, no dust and no biomass burning emissions. One experiment was run using 1970 emissions; no shipping emissions. Three experimental runs were performed using 2014 emissions with three different HO2 uptake coefficients; 0.1, 0.05 and 0 (no uptake). Surface data is archived for all simulations, additionally, data at pressure levels 200 hPa, and 500 hPa 800 hPa were archived for 2014.",
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            "title": "BIOARC: ground site real-time bioaerosol spectrometer datasets (2019-2021)",
            "abstract": "These datasets contain total, non-fluorescent and bio-fluorescent aerosol particle concentrations and particle size distributions collected with University of Manchester WIBS-4M an MBS-M spectrometers during the Towards a UK Airborne Bioaerosol Climatology (BIOARC) project.  \r\n\r\nData was collected at the following ground sites:\r\nCardington Meteorological Research Unit: MBS-M, 11/04/2019 - 09/06/2019\r\nChilbolton Observatory: WIBS-4D, 14/05/2019 - 14/06/2019\r\nWeybourne Atmospheric Observatory: WIBS-4M, 03/06/2019 - 01/08/2019\r\nChilbolton Observatory: WIBS-4M, 10/09/2020 - 21/06/2021\r\nWeybourne Atmospheric Observatory: MBS-M, 15/09/2020 - 03/11/2019\r\nWeybourne Atmospheric Observatory: MBS-M, 15/04/2021 - 16/07/2021\r\n\r\nNERC reference NE/S002049/1",
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            "uuid": "033cd690801741c9bc745b8da55faef4",
            "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.11 (v20220428)",
            "abstract": "Input Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 shows observed global temperature change over a wide range of timescales.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three subpanels. Input data are provided for panel b and panel c (lower panel).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Panel b:\r\n - gridded file of observed trends (as ASCII text) and significance overlay. Separate notes document.\r\n \r\n Panel c (lower panel):\r\n - Global surface temperature, relative to 1850 - 1900 for annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel b:\r\n - IndermediateData_Figure-2_11-HadCRUT_significance_overlay_1981-2020.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_significance_overlay_1900-1980.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_trends_1900-1980.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_trends_1981-2020.txt\r\n \r\n Panel c:\r\n - Figure_2_11c-lower_panel.csv; relates to the lower part of the figure. (black line, column 2, HadCRUT 5.0; cyan line, column 3, NOAA Global Temp; pink line, column 4, Berkeley Earth; orange line, column 5, Kadow et al.; grey shadow, columns 6 and 7, HadCRUT confidence limit)\r\n\r\nHadCRUT5 is a gridded dataset of global historical surface temperature anomalies relative to a 1961-1990 reference period produced by the Met Office Hadley Centre. \r\nNOAA Global Temp is a gridded dataset of global historical surface temperature anomalies relative to a 1971-2000 reference period produced by the National Oceanic and Atmospheric Administration. \r\nBerkeley Earth is a global historical land-ocean temperature index produced by Berkeley Earth.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Figure 2.11b - this is an ASCII grid (described in Figure_2_11-notes_on_HadCRUT_trend_files.txt) with a significance overlay. Should be approximately reproducible with any standard software to produce maps from gridded data.\r\n\r\n\r\nFigure 2.11c (lower panel), link to the code to reproduce this part of the figure is provided in the Related Documents section  of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo.",
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            "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Geostationary Operational Environmental Satellite (GOES) level 3U (L3U) product (2009-2020), version 1.00",
            "abstract": "This dataset contains land surface temperatures (LST) and their uncertainty estimates from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16.   The surface temperatures are generated every 3 hours for GOES 12 and 13 and every hour for GOES 16. Data are distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the GOES disk, which encompasses North and South America. \r\n\r\nLSTs are estimated from infrared measurements using a single channel algorithm in the case of GOES 12 and 13, and a split-window algorithm in the case of GOES 16. Observations are only available under clear-sky conditions. Quality of single channel algorithms is generally lower than dual channel ones, users are advised to read the respective Validation Report for more information on expected quality of these LST estimates.\r\n\r\nThe dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.",
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        {
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            "title": "Invisible Tracks: Collocation of wind-advected ship locations and shipping emissions with data from the MODIS cloud product",
            "abstract": "This dataset contains data from the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, collocated to wind-advected ship locations and shipping emissions. Most importantly, it includes effective droplet radii, calculated droplet number concentration, liquid water path, and cloud optical depth for locations where clouds have been polluted by shipping and to either side of a ship trajectory. Cloud data in the trajectory is labelled with the variable name only, data on either side additionally with [property]_1 and [property]_3 for the western and eastern side, respectively. The data is ungridded and comes in the form of csv files. It covers the period of 2014-2019. \r\n\r\nThe dataset is the product of three data sources: AIS data giving ship locations, ERA5 winds used to advect the emissions up to the time of the Aqua and Terra overpasses, as well as the level-2 cloud product MOD06.\r\n\r\nThis data was collected for the study of shipping aerosols' effect on marine liquid clouds, in particular when the emissions do not produce a satellite-visible ship track, which could be hand-logged.",
            "keywords": "cloud droplet, ship, MODIS",
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            "title": "ACSIS: Pan-Arctic sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with  modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with NCEP Reanalysis-2 atmospheric forcing data from 1980 - 2020",
            "abstract": "This dataset includes model output from a stand-alone ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) with an atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2) data (Kanamitsu et al., 2002, updated 2020). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: mixed-layer\r\nperiod: 1980-2020\r\natmospheric forcing: NCEP2\r\ndomain: pan-Arctic\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.",
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            "abstract": "This dataset includes model output from a stand-alone ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) with an atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2) data (Kanamitsu et al., 2002, updated 2020). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology\r\nocean model: mixed-layer\r\nperiod: 1980-2020\r\natmospheric forcing: NCEP2\r\ndomain: pan-Arctic\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.",
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            "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with DFS5.2 atmospheric forcing data from 1969 - 2015",
            "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: DFS5.2 (Dussin et al., 2016). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: NEMOv3.6\r\nperiod: 1969-2015\r\natmospheric forcing: DFS5.2 (Drakkar)\r\ndomain: global\r\ngrid resolution: 0.25deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.",
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            "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with modifications  including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with CORE II atmospheric forcing data from 1960 - 2009",
            "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: CORE II surface data (Large & Yeager, 2009). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: NEMOv3.6\r\nperiod: 1960-2009\r\natmospheric forcing: CORE II\r\ndomain: global\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.",
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            "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: CORE II surface data (Large & Yeager, 2009). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology\r\nocean model: NEMOv3.6\r\nperiod: 1960-2009\r\natmospheric forcing: COREII\r\ndomain: global\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.",
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            "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with DFS5.2 atmospheric forcing data from 1960 - 2015",
            "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This simulation uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. The atmospheric forcing data set are applied: DFS5.2 (Dussin et al., 2016). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: NEMOv3.6\r\nperiod: 1960-2015\r\natmospheric forcing: DFS5.2 (Drakkar)\r\ndomain: global\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.",
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            "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with  modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with NCEP Reanalysis-2 atmospheric forcing data from 1960 - 2015",
            "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) with an atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2) data (Kanamitsu et al., 2002, updated 2020). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: NEMOv3.6\r\nperiod: 1960-2015\r\natmospheric forcing: NCEP2\r\ndomain: global\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.",
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            "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci):  Global Plant Functional Types (PFT) Dataset, v2.0.8",
            "abstract": "This dataset contains Global Plant Functional Types (PFT) data, from the ESA Medium Resolution Land Cover (MRLC) Climate Change Initiative project.   The data provides yearly data, and initially covers the time period from 1992 to 2020.   It is anticipated that the dataset will be updated annually going forward.\r\n\r\nThe PFT v2.0.8 global dataset has 14 layers, each describing the percentage cover (0-100%) of a plant functional type at a spatial resolution of 300 m: broadleaved evergreen trees, broadleaved deciduous trees, needleleaved evergreen trees, needleleaved deciduous trees, broadleaved evergreen shrubs, broadleaved deciduous shrubs, needleleaved evergreen shrubs, needleleaved deciduous shrubs, natural grasses, herbaceous cropland (i.e., managed grasses), built, water, bare areas, and snow and ice.\r\n\r\n\"Plant Functional Types” (PFTs) refer to globally representative and similarly behaving plant types. PFTs can be related to physiognomy and phenology, climate (which defines the geographical ranges in which a plant type can grow and reproduce under natural conditions, and physiological activity (e.g., C3/C4 photosynthetic pathways).\r\n\r\nAll terrestrial zones of the Earth between the parallels 90°N and 90°S are covered. The PFT dataset has a regular latitude-longitude grid with a grid spacing of 0.002777777777778°, corresponding to ~300 m at the equator and ~200 m in the midlatitudes.    The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.\r\n\r\nThe plant functional type (PFT) distribution was created by combining auxiliary data products with the CCI MRLC map series. The LC classification provides the broad characteristics of the 300 m pixel, including the expected vegetation form(s) (tree, shrub, grass) and/or abiotic land type(s) (water, bare area, snow and ice, built-up) in the pixel. For some classes, the class legend specifies an expected range for the fractional covers of the contributing PFTs and broadly differentiates between natural and cultivated vegetation. We used a quantitative, globally consistent method that fuses the 300-metre MRLC product with a suite of existing high-resolution datasets to develop spatially explicit annual maps of PFT fractional composition at 300 metres. The new PFT product exhibits intraclass spatial variability in PFT fractional cover at the 300-metre pixel level and is complementary to the MRLC maps since the derived PFT fractions maintain consistency with the original LC class legend. \r\n\r\nThis dataset was generated to reduce the cross-walking component of uncertainty by adding spatial variability to the PFT composition within a LC class. This work moved beyond fine-tuning the cross-walking approach for specific LC classes or regions and, instead, separately quantifies the PFT fractional composition for each 300 m pixel globally. The result is a dataset representing the cover fractions of 14 PFTs at 300 m for each year within the time range, consistent with the CCI MRLC LC maps for the corresponding year.\r\n\r\nThis study was carried out with the continued support of the European Space Agency Climate Change Initiative under the contract ESA/No.4000126564 Land_Cover_cci.",
            "keywords": "Land Cover, CCI, PFT",
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            "dataLineage": "Data were processed by the ESA CCI Medium Resolution Land Cover project and catalogued here as part of the CCI Open Data Portal Project",
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                    "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_landcover_terms_and_conditions.pdf",
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        {
            "ob_id": 37341,
            "uuid": "031313dc6bfc4a0baabd58a51629bc21",
            "title": "CCMI-2022: REF-D1 data produced by the ACCESS-CM2-Chem model at CSIRO",
            "abstract": "This dataset contains model data for CCMI-2022 experiment refD1 produced by the ACCESS-CM2-Chem chemistry-climate model run by the modelling team at the CSIRO (Commonwealth Scientific and Industrial Research Organisation) ARCCSS (Australian Research Council Centre of Excellence for Climate System Science).\r\n\r\nThe refD1 experiment is a hindcast of the atmospheric state, using a prescribed evolution of sea surface temperature and sea ice from observations along with forcings for the extra-terrestrial solar flux, long-lived greenhouse gases and ozone depleting substances, stratospheric aerosols and an imposed quasi-biennial oscillation that approximate the observed variations over the historical period to the fullest extent possible.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n-  Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n-  A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30",
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            "dataPublishedTime": "2022-06-07T16:19:28",
            "doiPublishedTime": "2022-11-16T16:29:06",
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            "title": "Data to support Below-cloud scavenging of aerosol by rain: A review of numerical modelling approaches and sensitivity simulations with mineral dust",
            "abstract": "This dataset contains all the UM-GA8.0 climate model output needed to reproduce Table 2 and Figures 7-10 in the paper Below-cloud scavenging of aerosol by rain: A review of numerical modelling approaches and sensitivity simulations with mineral dust by Anthony C. Jones, Adrian Hill, John Hemmings, Pascal Lemaitre, Arnaud Querel, Claire L. Ryder, and Stephanie Woodward, Submitted to Atmospheric Chemistry and Physics, May 2022, as well as Figures S7-S13 in the Supplementary material. UM-GA8.0 is the Met Office Unified Model General Atmosphere vn8.0 in a climate configuration (N96L85) and using AMIP protocol (see Jones et al., 2022 for more details).\r\n\r\nAll files are CF-1.7 compliant and in NetCDF format with appropriate metadata. Each file contains monthly mean data for the 15 simulated years used in the paper (the 5 year spin up is not provided).\r\n\r\nThe files are separated into folders by experiment name:\r\n\r\nFolder      | Simulation name (Table 1 in Jones et al., 2022)\r\n-------------------------------------------------------------\r\nSlinn       | Slinn\r\nSlinnph     | Slinn+ph\r\nSlinnphrc   | Slinn+ph+rc\r\nWang        | Wang\r\nLaakso      | Laakso\r\nSlinnPhRc1M | Slinn+ph+rc(1M)\r\nSlinnPhRcDm | Slinn+ph+rc(dm)\r\nLaaksoDm    | Laakso(dm)\r\n\r\nThe files use standard CMIP naming conventions with one slight modification: before the 'nc' suffix, the aerosol mode that the variable applies to is generally given (either coarse (cor) or accumulation (acc) mode). The STARTDATE and ENDDATES are the same for all files (12/1993 and 11/2008 respectively). As is the TIMEPERIOD (Amon, i.e., monthly mean data) and the MODEL (MetUM, else known as UM-GA8.0). \r\n\r\nVARIABLE_TIMEPERIOD_MODEL_EXPERIMENT_STARTDATE_ENDDATE.MODE.nc\r\n\r\nThe variables comprise:\r\n\r\nShort name  | Description (units, if any)\r\n-------------------------------------------------------------\r\nconccn      | Particle number concentration (m-3)\r\nconcdust    | Particle mass concentration (kg.m-3)\r\ndiamdust    | Modal median diameter (m)\r\ndrydust     | Dust dry deposition rate (kg.m-2.s-1)\r\nemidust     | Dust surface emission rate (kg.m-2.s-1)\r\nloaddust    | Vertically integrated dust load (kg.m-2)\r\nmmratedust  | Dust mode-merging (cor->acc) rate (kg.m-3.s-1)\r\nod443dust   | Dust optical depth at 443 nm\r\nod550dust   | Dust optical depth at 443 nm\r\norog        | Surface Altitude (m)\r\nwetdust     | Dust wet deposition rate (kg.m-2.s-1)\r\nzfull       | Model altitude at the top of the gridcell (m)\r\n\r\nThe mass concentration (concdust), number concentration (conccn), and mass burden (loaddust) were calculated from monthly-mean mass or number mixing ratios and monthly mean potential temperature, pressure, and specific humidity fields (not supplied). The mode mixing rate (mmratedust) is only available for the downard mode merging simulations (SlinnPhRcDm and LaaksoDm).\r\n\r\nAll figures in the paper were produced using Python (3.6) and Iris scientific analysis software.\r\n\r\nAll data is Crown Copyright, Met Office, and is made available under the terms of the Non-Commercial Government Licence: http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/",
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        {
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            "uuid": "1fcf16f9d33348589d4e8431083470d0",
            "title": "CCMI-2022: senD2-sai data produced by the NIWA-UKCA2 model at NIWA",
            "abstract": "This dataset contains model data for CCMI-2022 experiment senD2-sai produced by the NIWA-UKCA2 chemistry-climate model run by the modelling team at NIWA (National Institute of Water and Atmospheric Research) in New Zealand.\r\n\r\nThe senD2-sai simulation is based on the refD2 experiment but with a modified specified stratospheric aerosol distribution reflecting increased stratospheric aerosol amounts from stratospheric aerosol injection (SAI). Sea ice and sea surface temperatures (SSTs) are specified to follow a repeating annual cycle taken from those used by the same model for their refD2 experiment over 2020 - 2030, the period when SAI is assumed to have been initiated.\r\n\r\nThe refD2 experiment is the baseline projection for updated projections of ozone recovery. Specified forcings largely following the same specifications as for the SSP2-4.5 scenario of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with the exception of the near-surface mixing ratio of Ozone Depleting Substances which follow the baseline projection from WMO (2018).\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\nSSP2-4.5 is a Shared Socio-economic Pathway scenario that follows socio-economic storyline SSP2 with intermediate mitigation and adaptation challenges and climate forcing pathway RCP4.5 which leads to a radiative forcing of 4.5 Wm-2 by the year 2100.\r\nWMO-2018 refers to the Scientific Assessment of Ozone Depletion: 2018.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n-  Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n-  A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30",
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178495/?format=api",
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178498/?format=api",
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            "ob_id": 37365,
            "uuid": "9fb47f1d6d294b0dba553adc2253e6cf",
            "title": "Idealised climate model simulations for the Iceland Greenland Sea's Project (IGP)",
            "abstract": "This dataset contains idealised climate model simulations for the Atmospheric Forcing of the Iceland Sea (AFIS) project  which was the UK component, funded by NERC, of the Iceland Greenland Sea's Project (IGP).  The UK Met Office Unified Model (MetUM) version 10.6 with a regional nested domain was used to carry out a suite of simulations of the atmosphere over the NE North Atlantic region. The set up of the MetUM uses the Global Atmosphere 6 and Global Land 6 (GA6/GL6)  configurations including the ENDGame dynamical core.\r\nModel simulations were run on the Met Office supercomputer accessed through Monsoon.  \r\n\r\nThis dataset contains the output for one of 7 model outputs. \r\nThese are:\r\nTemperature - 1.5m Surface Temperature output from themodel simulations.\r\nLatent HF - Latent Heat Flux output from the model simulations.\r\nSensible HF - Sensible Heat Flux output from the model simulations.\r\nRelative Humidity - Relative Humidity output from the model simulations.\r\nSpecific Humidity - Specific Humidity output from the model simulations.\r\nSurface Winds - 10 m U and V wind component output from the model simulations.\r\nMSLP - Mean Sea Level Pressure output from the model simulations.\r\n\r\nA list of papers related to this dataset can be found in the linked online resources on this record.",
            "keywords": "IGP, AFIS, MetUM",
            "publicationState": "published",
            "dataPublishedTime": "2023-08-10T14:17:11",
            "doiPublishedTime": null,
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            "status": "ongoing",
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                "dataPath": "/badc/igp/data/IGP_climate_model/",
                "oldDataPath": [],
                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 136695138100,
                "numberOfFiles": 457,
                "fileFormat": "Data are NetCDF formatted.\r\n\r\nFiles ending _daymean.nc or ydaymean.nc were processed using CDO.  the CDO command that the filename ends with.  IE for MSLP, the file u_au087_MSLP_9110_daymean.nc was created using\r\n\r\ncdo daymean u_au087_MSLP_9110.nc u_au087_MSLP_9110_daymean.nc\r\n\r\nThe folder splitmon used the CDO command splitmon to break each file down into January, February, March and April, identified by the suffix 01, 02, 03 and 04 respectively.  Daymean and ydaymean versions were created."
            },
            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/11210/?format=api",
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                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/70872/?format=api"
            ],
            "dataLineage": "The model simulations were run by James Pope when he was employed on the IGP project at the British Antarctic Survey.  Model simulations were run on the Met Office supercomputer accessed through Monsoon.  As of July 2019, James Pope is employed by the Met Office and can be contacted at james.pope@metoffice.gov.uk. These data were then supplied to CEDA for archiving.",
            "removedDataTime": null,
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            "language": "English",
            "identifier_set": [],
            "projects": [
                "https://api.catalogue.ceda.ac.uk/api/v2/projects/24899/?format=api"
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                "https://api.catalogue.ceda.ac.uk/api/v2/observationcollections/27445/?format=api"
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178517/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178518/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178519/?format=api"
            ],
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            "procedureComputation": "https://api.catalogue.ceda.ac.uk/api/v2/computations/37367/?format=api",
            "permissions": [
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                    "ob_id": "https://api.catalogue.ceda.ac.uk/api/v2/observations/2543/?format=api",
                    "useLimitation": null,
                    "accessConstraints": null,
                    "accessCategory": "registered",
                    "accessRoles": null,
                    "label": "registered: None group",
                    "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf",
                    "licenceClassifications": "unstated"
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        {
            "ob_id": 37368,
            "uuid": "c5843f2ee17b417f87b75b6fb2b274ac",
            "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Spinning Enhanced Visible and Infrared Imager (SEVIRI) on MSG level 3 (L3U) product (2004-2020), version 3.00",
            "abstract": "This dataset contains land surface temperatures (LST) and their uncertainty estimates from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation series (Meteosat 8 - 11, also known as MSG1-4). The surface temperatures are generated every hour and distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the MSG disk, which encompasses Europe, Africa and part of South America. \r\n\r\nLSTs are estimated from infrared measurements using a split-window algorithm and, therefore, are only available under clear-sky conditions. \r\n\r\nThe dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.",
            "keywords": "land surface temperature, CCI, GOES",
            "publicationState": "preview",
            "dataPublishedTime": null,
            "doiPublishedTime": null,
            "updateFrequency": "notPlanned",
            "status": "pending",
            "result_field": null,
            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/10319/?format=api",
            "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/3466/?format=api",
            "nonGeographicFlag": false,
            "phenomena": [],
            "dataLineage": "The data record has been produced by the Portuguese Institute for Sea and Atmosphere (IPMA) within the ESA Land Surface Temperature Climate Change Initiative (LST_cci) and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). Level 1 satellite observations were provided by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF).",
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        {
            "ob_id": 37370,
            "uuid": "c6e27bda1fc849c098a7fff7ff69fd5a",
            "title": "Halkali Agricultural School (Istanbul, Turkey): Daily Meteorological Observations 1896-1917",
            "abstract": "Daily weather observations measured by students and staff at Halkali Agricultural School (a school opened in 1892 for agriculture and animal husbandry during the Ottoman period) from 1896 to 1917 in Istanbul, Turkey have been transcribed from the original publications into digital form and translated from Ottoman Turkish (the Perso-Arabic script) to English (Latin alphabet). Over 55 thousand observations of daily maximum, minimum and average temperature, rainfall, soil and under soil (0.25m) temperature, humidity, pressure, and wind speed were recovered.",
            "keywords": "Meteorology",
            "publicationState": "citable",
            "dataPublishedTime": "2022-06-08T10:23:13",
            "doiPublishedTime": "2022-06-08T10:56:10",
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            "status": "completed",
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                "dataPath": "/badc/deposited2022/Ottoman_data_halkali_1896_1917",
                "oldDataPath": [],
                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 694017645,
                "numberOfFiles": 275,
                "fileFormat": "Data are BADC-CSV and JPEG formatted."
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            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/10320/?format=api",
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            "nonGeographicFlag": false,
            "phenomena": [],
            "dataLineage": "The Halkal Agricultural School paid increased attention to meteorological observations and weather forecasts due to the weather's significance in agriculture. Since late 1896 until 1917, students and faculty at Halkali have measured numerous parameters, including daily maximum, minimum, and average temperature, precipitation, soil and subsoil (0.25m) temperature, humidity, pressure, wind speed, wind direction, and weather status. The primary objective of these observations was to conduct agricultural activities under suitable weather conditions.",
            "removedDataTime": null,
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            "language": "English",
            "identifier_set": [
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178537/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178538/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178539/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178544/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178545/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178546/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178547/?format=api"
            ],
            "procedureAcquisition": "https://api.catalogue.ceda.ac.uk/api/v2/acquisitions/37373/?format=api",
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                    "accessCategory": "public",
                    "accessRoles": null,
                    "label": "public: None group",
                    "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/",
                    "licenceClassifications": "any"
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                    "ob_id": 1138,
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        {
            "ob_id": 37377,
            "uuid": "d215ebefe04546088a14dfc7ffc0643f",
            "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multi-Functional Transport Satellite (MTSAT) level 3U (L3U) product (2009-2015), version 1.00",
            "abstract": "This dataset contains land surface temperatures (LST) and their uncertainty estimates from the Japanese Advanced Meteorological Imager (JAMI) onboard the Multi-Functional Transport Satellite series (MTSAT1 and 2, also known as Himiwari-6 and 7). The surface temperatures are generated every 3 hours and distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the MTSAT disk, which encompasses Australia and part of Asia.\r\n\r\nThe LSTs in this dataset are estimated from infrared measurements using a single channel algorithm, and, therefore, are only available under clear-sky conditions. The quality of single channel algorithms is generally lower than dual channel ones, and users are advised to read the respective Validation Report for more information on the expected quality of these LST estimates.\r\n\r\nThe dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.",
            "keywords": "land surface temperature, CCI, MTSAT",
            "publicationState": "citable",
            "dataPublishedTime": "2023-05-16T13:37:25",
            "doiPublishedTime": "2023-05-17T08:29:38",
            "updateFrequency": "notPlanned",
            "status": "completed",
            "result_field": {
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                "dataPath": "/neodc/esacci/land_surface_temperature/data/MTSAT_JAMI/L3U/v1.00/",
                "oldDataPath": [],
                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 225779044268,
                "numberOfFiles": 17037,
                "fileFormat": "Data are in NetCDF format"
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            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/10321/?format=api",
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            "dataLineage": "The data record has been produced by the Portuguese Institute for Sea and Atmosphere (IPMA) within the ESA Land Surface Temperature Climate Change Initiative (LST_cci) and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). Level 1 satellite observations were provided by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF). \r\nContact: data.lst-cci@acri-st.fr",
            "removedDataTime": null,
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            "language": "English",
            "identifier_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/12505/?format=api"
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178561/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178562/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178563/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178557/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178564/?format=api"
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            "procedureAcquisition": null,
            "procedureCompositeProcess": "https://api.catalogue.ceda.ac.uk/api/v2/composites/37379/?format=api",
            "procedureComputation": null,
            "permissions": [
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                    "useLimitation": null,
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                    "accessCategory": "public",
                    "accessRoles": null,
                    "label": "public: None group",
                    "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf",
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        {
            "ob_id": 37381,
            "uuid": "a07deacaffb8453e93d57ee214676304",
            "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 2.0.2",
            "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.0.2 of the dataset.   \r\n\r\nThe five thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website  and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z",
            "keywords": "ESA, CCI, Lakes, ECV",
            "publicationState": "citable",
            "dataPublishedTime": "2022-07-06T09:44:33",
            "doiPublishedTime": "2022-07-06T09:45:15",
            "updateFrequency": "notPlanned",
            "status": "completed",
            "result_field": {
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                "dataPath": "/neodc/esacci/lakes/data/lake_products/L3S/v2.0.2/",
                "oldDataPath": [],
                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 519722961950,
                "numberOfFiles": 10325,
                "fileFormat": "Data are in NetCDF format"
            },
            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/10175/?format=api",
            "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/2623/?format=api",
            "nonGeographicFlag": false,
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        {
            "ob_id": 37385,
            "uuid": "4394898334094551bfb29fb37d2f054c",
            "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.2 (v20220610)",
            "abstract": "Data for Figure 3.2 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.2 shows changes in surface temperature for different paleoclimates.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three subpanels, the data provided for all panels in subdirectories named panel_a, panel_b, panel_c\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For panel (a):\r\n - PMIP3 global temperature anomalies over continents and oceans reconstruction sites\r\n - PMIP4 CMIP6 global temperature anomalies over continents and oceans reconstruction sites\r\n - PMIP4 non-CMIP6 global temperature anomalies over continents and oceans reconstruction sites\r\n - Tierney 2020 reconstructions of marine temperature\r\n - Cleator 2020 reconstructions of continental temperature\r\n \r\n For panel (b):\r\n - CMIP5 temperature data for paleoclimate periods\r\n - CMIP6 temperature data for paleoclimate periods\r\n - non-CMIP temperature data for paleoclimate periods\r\n - Instrumental observational and observations from reconstructions\r\n \r\n For panel (c):\r\n - Volcanic forcing from TS17, CU12, GRA08\r\n - CMIP6 GMST anomaly with respect to 1850-1900 modelled with TS17 volcanic forcing\r\n - CMIP5 GMST anomaly with respect to 1850-1900 modelled with CU12 volcanic forcing\r\n - CMIP5 GMST anomaly with respect to 1850-1900 modelled with GRA08 volcanic forcing\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/temperature_anomalies_scatter_points.csv relates to the scatter points and their standard deviation for panel (a)\r\n - For panel (b) the datasets are stored as following panel_b/temperature_{color}_{marker}_{period}_{model_group}_{additional_info}.csv and relates to the scatter points for panel (b).\r\n - For panel (c) the data is stored in panel_c/gmst_changes_paleo_volcanic_forcings.csv and relates to red, green, blue and black lines on the panel as well as grey shadings.\r\n Additional information about data provided in relation to figure in files headers.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nPMIP4 is the Paleoclimate Modelling Intercomparison Project phase 4\r\nPMIP3 is the Paleoclimate Modelling Intercomparison Project phase 3\r\n\r\n ---------------------------------------------------\r\n Temporal Range of Paleoclimate Data\r\n ---------------------------------------------------\r\n This dataset covers a paleoclimate timespan from 56 Ma (56 million years ago) to 2010.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data.\r\n ---------------------------------------------------\r\n For panel (a) the error bar should be plotted as anomalies from columns 2/4 +/- standard deviation. \r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website.",
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            "dataPublishedTime": "2022-06-24T15:47:18",
            "doiPublishedTime": "2023-07-03T13:07:25.976938",
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            "abstract": "Data for Figure 3.7 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.7 shows regression coefficients and corresponding attributable warming estimates for individual CMIP6 models.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c and panel_d.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains information on global temperature attributable warming (2010-2019 relative to 1850-1900) from CMIP6 models: \r\n \r\n - Regression coefficients for two way regression (2010-2019 relative to 1850-1900)\r\n - Regression coefficients for three way regression (2010-2019 relative to 1850-1900)\r\n - Attributable warming for two way regression (2010-2019 relative to 1850-1900)\r\n - Attributable warming for three way regression (2010-2019 relative to 1850-1900)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/regression_coeff_two_way_regression.csv has data for brown and green crosses\r\n - panel_b/regression_coeff_three_way_regression.csv has data for grey, green and blue crosses\r\n - panel_c/attributable_warming_two_way_regression.csv has data for brown and green crosses\r\n - panel_d/attributable_warming_three_way_regression.csv has data for grey, green and blue crosses\r\n\r\n Details about the data provided in relation to the figure in the header of every file.\r\n\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.",
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