Timeline / Data.gov — Climate Datasets
changed Source changed
A new raw object was archived. Both versions are preserved. 25 line(s) added, 25 line(s) removed.
Evidence
| Source | Data.gov — Climate Datasets |
|---|---|
| Agency | Data.gov |
| URL | https://api.gsa.gov/technology/datagov/v4/search?q=climate&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY} |
| Observed by | Civic Memory, directly, on 2026-09-12T18:27:55+00:00 |
| Content type | application/json |
| Current object |
4fa7450a3ac860e9116b6b3ef4c4d77499770874c7b64bd5766042e87a81a5be
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metadata
|
| Previous object |
705f43d43e7f7c044074b3e8833b660f1a6931b7e97e9fc272cd27dfcdb962da
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What changed derived
This diff is not evidence. It was produced by
civic-memory.diff_engine 1.1.0 at
2026-09-12T18:27:55+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 25 line(s) added, 25 line(s) removed.
--- previous +++ current @@ -286,10 +286,10 @@ "type": "dataset" }, { - "_score": 58.784195, + "_score": 58.784218, "_sort": [ 1789172720076, - 58.784195, + 58.784218, 1, "1b47995f-8fb7-4561-8a50-138495dbcd3c" ], @@ -428,10 +428,10 @@ "type": "dataset" }, { - "_score": 42.45816, + "_score": 42.458168, "_sort": [ 1789172709710, - 42.45816, + 42.458168, 0, "d6477f94-e7eb-421c-94f1-946c41bb4d1b" ], @@ -702,10 +702,10 @@ "type": "dataset" }, { - "_score": 20.210724, + "_score": 20.210741, "_sort": [ 1789172563104, - 20.210724, + 20.210741, 0, "610744f6-e148-447a-ae3c-220f4452d0d5" ], @@ -1105,10 +1105,10 @@ "type": "dataset" }, { - "_score": 59.873695, + "_score": 59.873722, "_sort": [ 1789172392353, - 59.873695, + 59.873722, 1, "f4275485-1d12-4073-9635-208abaef3e99" ], @@ -1397,10 +1397,10 @@ "type": "dataset" }, { - "_score": 31.487131, + "_score": 31.487164, "_sort": [ 1789172289019, - 31.487131, + 31.487164, 0, "76a59cf2-7564-45b1-bd99-62fc3a0ac451" ], @@ -1666,10 +1666,10 @@ "type": "dataset" }, { - "_score": 58.784195, + "_score": 58.784218, "_sort": [ 1789171005887, - 58.784195, + 58.784218, 5, "6319c1d7-b70e-496e-8ce8-720a4e18a616" ], @@ -2520,10 +2520,10 @@ "type": "dataset" }, { - "_score": 46.57521, + "_score": 46.575203, "_sort": [ 1789169506408, - 46.57521, + 46.575203, 0, "38fd93e0-e42d-4b24-8ce6-18e4b051d828" ], @@ -2934,10 +2934,10 @@ "type": "dataset" }, { - "_score": 29.000114, + "_score": 29.000135, "_sort": [ 1789169166232, - 29.000114, + 29.000135, 0, "e62467ce-c2d6-49c8-814f-93a13e7ee1b4" ], @@ -3639,10 +3639,10 @@ "type": "dataset" }, { - "_score": 57.779682, + "_score": 57.779705, "_sort": [ 1789168220771, - 57.779682, + 57.779705, 0, "d589c767-50b0-47cb-8244-1f136554a7b8" ], @@ -3917,10 +3917,10 @@ "type": "dataset" }, { - "_score": 57.17027, + "_score": 57.170288, "_sort": [ 1789167405556, - 57.17027, + 57.170288, 0, "e3016848-09d9-49ad-811d-52717efcf149" ], @@ -4215,10 +4215,10 @@ "type": "dataset" }, { - "_score": 8.763033, + "_score": 8.76302, "_sort": [ 1789080651201, - 8.763033, + 8.76302, 2, "44910ba5-b6c4-4494-9313-ae768358536c" ], @@ -5647,10 +5647,10 @@ "type": "dataset" }, { - "_score": 30.756447, + "_score": 30.756475, "_sort": [ 1789080634405, - 30.756447, + 30.756475, 4, "dda12070-8ac5-4af6-818c-3cdf506cb347" ], @@ -5919,4 +5919,4 @@ ], "title": "Downscaled Climate Projections for the Edwards Aquifer Region (EAR) using CMIP5 for the years 2006 – 2100 and CMIP6 for the years 2015 – 2100" }, - "description": "Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections and 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1) and six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005 for CMIP5 and 1980-2014 for CMIP6) and future projections (2006-2100 for CMIP5 and 2015-2100 for CMIP6) are provided.\nApplied researchers may explore aspects of potential changes in + "description": "Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections and 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1) and six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005 for CMIP5 and 1980-2014 for CMIP6) and future projections (2006-2100 for CMIP5 and 2015-2100 for CMIP6) are provided.\nApplied researchers may explore aspects of potential change