Google Earth Engine ——1958-2020年TerraClimate 全球陆地表面每月气候和气候水平衡的数据集

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TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho

TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces. It uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser spatial resolution, but time-varying data from CRU Ts4.0 and the Japanese 55-year Reanalysis (JRA55). Conceptually, the procedure applies interpolated time-varying anomalies from CRU Ts4.0/JRA55 to the high-spatial resolution climatology of WorldClim to create a high-spatial resolution dataset that covers a broader temporal record.

Temporal information is inherited from CRU Ts4.0 for most global land surfaces for temperature, precipitation, and vapor pressure. However, JRA55 data is used for regions where CRU data had zero climate stations contributing (including all of Antarctica, and parts of Africa, South America, and scattered islands). For primary climate variables of temperature, vapor pressure, and precipitation, the University of Idaho provides additional data on the number of stations (between 0 and 8) that contributed to the CRU Ts4.0 data used by TerraClimate. JRA55 was used exclusively for solar radiation and wind speeds.

TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. A modified Thornthwaite-Mather climatic water-balance model and extractable soil water storage capacity data was used at a 0.5° grid from Wang-Erlandsson et al. (2016).

TerraClimate 是全球陆地表面每月气候和气候水平衡的数据集。它使用气候辅助插值，将 WorldClim 数据集的高空间分辨率气候法线与较粗的空间分辨率、但来自 CRU Ts4.0 和日本 55 年再分析 (JRA55) 的时变数据相结合。从概念上讲，该过程将来自 CRU Ts4.0/JRA55 的内插时变异常应用于 WorldClim 的高空间分辨率气候学，以创建涵盖更广泛时间记录的高空间分辨率数据集。大多数全球陆地表面的温度、降水和蒸汽压力的时间信息继承自 CRU Ts4.0。但是，JRA55 数据用于 CRU 数据具有零气候站贡献的地区（包括整个南极洲、非洲部分地区、南美洲和分散的岛屿）。对于温度、蒸气压和降水等主要气候变量，爱达荷大学提供了额外的数据，涉及对 TerraClimate 使用的 CRU Ts4.0 数据做出贡献的站点数量（0 到 8 之间）。 JRA55 专门用于太阳辐射和风速。 TerraClimate 还使用水平衡模型生成月度地表水平衡数据集，该模型结合了参考蒸散量、降水、温度和内插植物可提取土壤水容量。修改后的 Thornthwaite-Mather 气候水平衡模型和可提取的土壤蓄水能力数据在 Wang-Erlandsson 等人的 0.5° 网格中使用。 (2016)。

Dataset Availability

1958-01-01T00:00:00 - 2020-12-01T00:00:00

Dataset Provider

University of California Merced

Collection Snippet

ee.ImageCollection("IDAHO_EPSCOR/TERRACLIMATE")

Resolution

2.5 arc minutes

Bands Table

Name	Description	Min*	Max*	Units	Scale
aet	Actual evapotranspiration, derived using a one-dimensional soil water balance model	0	3140	mm	0.1
def	Climate water deficit, derived using a one-dimensional soil water balance model	0	4548	mm	0.1
pdsi	Palmer Drought Severity Index	-4317	3418		0.01
pet	Reference evapotranspiration (ASCE Penman-Montieth)	0	4548	mm	0.1
pr	Precipitation accumulation	0	7245	mm	0
ro	Runoff, derived using a one-dimensional soil water balance model	0	12560	mm	0
soil	Soil moisture, derived using a one-dimensional soil water balance model	0	8882	mm	0.1
srad	Downward surface shortwave radiation	0	5477	W/m^2	0.1
swe	Snow water equivalent, derived using a one-dimensional soil water balance model	0	32767	mm	0
tmmn	Minimum temperature	-770	387	°C	0.1
tmmx	Maximum temperature	-670	576	°C	0.1
vap	Vapor pressure	0	14749	kPa	0.001
vpd	Vapor pressure deficit	0	1113	kPa	0.01
vs	Wind-speed at 10m	0	2923	m/s	0.01

* = Values are estimated

影像属性：

Name	Type	Description
status	String	'provisional' or 'permanent'

数据说明：

The data set is in the public domain as licensed under the Creative Commons Public Domain (CC0) license.

数据引用：

Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch, 2018, Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015, Scientific Data 5:170191, doi:10.1038/sdata.2017.191

代码：

var dataset = ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE')
                  .filter(ee.Filter.date('2017-07-01', '2017-08-01'));
var maximumTemperature = dataset.select('tmmx');
var maximumTemperatureVis = {
  min: -300.0,
  max: 300.0,
  palette: [
    '1a3678', '2955bc', '5699ff', '8dbae9', 'acd1ff', 'caebff', 'e5f9ff',
    'fdffb4', 'ffe6a2', 'ffc969', 'ffa12d', 'ff7c1f', 'ca531a', 'ff0000',
    'ab0000'
  ],
};
Map.setCenter(71.72, 52.48, 3);
Map.addLayer(maximumTemperature, maximumTemperatureVis, 'Maximum Temperature');