A Comprehensive Evaluation of Surface Air Temperature Reanalyses over China against Urbanization Bias–Adjusted Observations

TLDR: In this article, the authors compare the differences in surface air temperature (SAT) between observational that has been adjusted for urbanization bias and reanalysis data (NCEPV1, N CEPV2, ERA5, CFSR, MERRA, JRA55, 20CRV3 and CRA40) over mainland China during 1961-2015.

About: This article is published in Advances in Climate Change Research.The article was published on 2021-02-12 and is currently open access. It has received 13 citations till now.

Figures

Table 5. Ranking and score of each of the reanalysis datasets in mainland China for the periods 742 1961–2015 and 1979–2015 743

Figure 1. Distribution of the elevation distribution and 763 observation stations across mainland 748 China. (The insert shows the number of stations at various elevations, the asl stands for elevation 749 (units: m), the red bar stands for the number of the elevation distribution) 750

Table 1. Atmospheric reanalysis datasets applied in this study 722

Figure 5. Annual mean SAT and annual mean SAT linear trends for the periods 1961–2015 and 768 1979–2015. (The red bar stand for climate state (units: °C), the orange bar stands for linear trends 769 (units: °C/10 years)) 770

Figure 4. Wavelet analysis based on REAs and OBS from 1961–2015: (a) NCEPV1; (b) JRA55; 763 (c) 20CRV3; (d) CRA40; (e) NCEPV2; (f) ERA5; (g) CFSR ; (h) MERRA ; (i) OBS. 764

Table 2. Relative bias between the REAs and OBS for climate state and linear trends for the 726 periods 1961–2015 and 1979–2015 727

Frequently asked questions

Q1. How many reanalysis datasets have been developed?

The biases in 70 inter–annual variability, time series, and spatial distribution characteristics between reanalysis and 71 observational data have been analyzed. 

Q2. What is the overall performance of the REAs in the period 1979–2015?

The integrated score of 469 20CRV3 was 92.5 and 93.5 during the periods of 1979–2015 and 1961–2015 respectively, with the 470 better performance than other REAs in linear trend (1979–2015), dispersion (1961–2015), and 471 climate state (1961–2015). 

Q3. What are the disadvantages of the reanalysis?

Since these reanalyses have the disadvantages of incorporating the errors from 53 the numerical prediction models, the assimilation processes, and the observation systems 54 (Bengtsson et al., 2004a, 2004b; Zhao et al., 2010; Zhao et al., 2015), the reliability of the reanalysis 55 data needs to be proven on global and regional scales. 

Q4. How did the authors evaluate the applicability of the reanalysis data?

In this study, the authors evaluate the applicability of the reanalysis data by using the urbanization 125 bias–adjusted observational data from 1961–2015 across mainland China. 

Q5. What are the REA biases in correlation, standard deviation, climate state and linear trends?

527 (4) The REA biases in correlation, standard deviation, climate state and linear trends generally 528 increase with increasing elevation of stations. 

Q6. What is the average of the data used in the reanalysis?

The time series, correlation, standard deviation, 177 climate state, and linear trends of the reanalysis data are evaluated against the observations. 

Q7. What are the reasons for the lower SATs of CRA40, NCEPV1?

The 512 cold biases of CRA40, NCEPV1 and JRA55 are mainly caused by the lower SAT in summer, 513 whereas those of NCEPV2, CFSR, and ERA5 are mainly caused by the lower winter mean 514 temperature.