# Title of Dataset
---
Data used for a meta-analysis of warming effects on soil temperature, soil mositure, ecosystem C flux, vegetation biomass, soil C, microbial biomass, soil N, and green leaf N

## Description of the data and file structure

Table S2 Data on soil temperature, soil mositure, ecosystem C flux, vegetation biomass, soil C, microbial biomass, and soil N

1)	The data collected for meta-analysis were all from warming experiments conducted in alpine, subarctic, and arctic biomes of the northern hemisphere. As we focused on air warming effects, only warming experiments used methods of infrared radiators (IR), open-top chambers (OTCs), snow fences, and greenhouse were included. Thus, we did not included soil warming experiments using soil heating cables into analysis as Wang et al., 2019 (See Wang et al., 2019, Effects of climate warming on carbon fluxes in grasslands - a global meta-analysis. Global Change Biology).
2)	Note that to test for differences in responses of functional groups, vegetation types were classified to moss, herb, herb and shrub, and shrub. 
3)	Soil moisture status was characterized to dry, moist, and wet. For sites where soil moisture status was not specified (e.g. Fu et al, 2012, 2013, 2015, 2018, and 2019), we characterized it based on soil texture, soil moisture content, and water holding capacity. Soil moisture status was identified according to the ratio of soil moisture content to water holding capacity (<60%: dry, 60—100%: moist, >100%, wet). 
4)	As reports on non-growing season warming effects for alpine and arctic ecosystems were rather limited (e.g. Fu et al., 2019), we took only growing-season and year-round warming into consideration.
5)	Air temperature increases varied from 0.2 to 6.2 ℃, with 2℃ chosen as the subdividing point (low warming: ≤2℃, high warming: > 2℃). For some studies that did not report warming effects on air temperature (e.g. Li et al., 20017), we characterzied warming levels according to soil temperature increase. If soil temperature increase exceeded 1.6 ℃, the treatment was considered as high-level warming. 
6)	The warming time ranged from 1 to 21 growing seasons, and was grouped into short- and long-term warming subdivided by 3 growing seasons. 
7)	Admittedly, warming approaches could affect warming effects (Wang et al., 2019, see above). Unlike IR, passive warming, such as OTC, has actually no capability to set a fixed temperature varying from few to several degrees along the day, and increases temperature only during daytime. However, a recent study has confirmed that warming methods have no differences in their effects on alpine AGB, BGB, SOC, MBC, and TN (Chen et al., 2020, Effects of warming on carbon and nitrogen cycling in alpine grassland ecosystems on the Tibetan Plateau: A meta-analysis. Geodera, 370, 114363). Thus, in the current study we did not analyze their effects. 
8)	GEP—gross ecosystem productivity, ER—ecosystem respiration, NEE—net ecosystem C exchange, AGB—aboveground biomass, BGB—belowground biomass, SOC—soil organic C, and MBC—microbial biomass.
9）          n/a: No data
10）        Xt, St, and nt denote the mean, standard deviation, and sample size in the experimental group, and Xc, Sc, and nc denote the mean,standard deviation, and sample size in the control group

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Table S3 Data on green leaf N
In table S3, Xt, St, and nt denote the mean, standard deviation, and sample size in the experimental group, and Xc, Sc, and nc denote the mean,standard deviation, and sample size in the control group
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## Sharing/Access information

Links to other publicly accessible locations of the data:
  * https://orcid.org/0000-0002-5479-9626

Data was derived from the following sources:
  * https://doi.org/10.5061/dryad.jdfn2z3c1