Articles | Volume 13, issue 4
Research article
18 Nov 2022
Research article |  | 18 Nov 2022

Regional dynamical and statistical downscaling temperature, humidity and wind speed for the Beijing region under stratospheric aerosol injection geoengineering

Jun Wang, John C. Moore, Liyun Zhao, Chao Yue, and Zhenhua Di

We use four Earth system models (ESMs) to simulate climate under the modest greenhouse emissions RCP4.5 (Representative Concentration Pathway), the “business-as-usual” RCP8.5 and the stratospheric aerosol injection G4 geoengineering scenarios. These drive a 10 km resolution dynamically downscaled model (Weather Research and Forecasting, WRF) and a statistically bias-corrected (Inter-Sectoral Impact Model Intercomparison Project, ISIMIP) and downscaled simulation in a 450×330 km domain containing the Beijing Province, ranging from 2000 m elevation to sea level. The 1980s simulations of surface temperatures, humidities and wind speeds using statistical bias correction make for a better estimate of mean climate determined by ERA5 reanalysis data than does the WRF simulation. However correcting the WRF output with quantile delta mapping bias correction removes the offsets in mean state and results in WRF better reproducing observations over 2007–2017 than ISIMIP bias correction. The WRF simulations consistently show 0.5 C higher mean annual temperatures than from ISIMIP due both to the better resolved city centres and also to warmer winter temperatures. In the 2060s WRF produces consistently larger spatial ranges of surface temperatures, humidities and wind speeds than ISIMIP downscaling across the Beijing Province for all three future scenarios. The WRF and ISIMIP methods produce very similar spatial patterns of temperature with G4 and are always cooler than RCP4.5 and RCP8.5, by a slightly larger amount with ISIMIP than WRF. Humidity scenario differences vary greatly between ESMs, and hence ISIMIP downscaling, while for WRF the results are far more consistent across ESMs and show only small changes between scenarios. Mean wind speeds show similarly small changes over the domain, although G4 is significantly windier under WRF than either RCP scenario.

1 Introduction

The global-mean surface air temperature has increased by 0.9–1.2 C relative to 1850–1900 (Eyring et al., 2021), with a rapid rise during the 2010s. Extreme climate events are becoming more frequent (Pachauri et al., 2014), impacting human health and mortality rates (Pielke et al., 2013). Earth system models (ESMs), despite being global in extent, cannot simulate phenomena smaller than their spatial resolution (typically 1–2 ) with the same fidelity as higher-resolution models with much smaller domains. Higher-resolution models include regional climate models and the Weather Research and Forecasting model (WRF) which are generally driven by ESMs at their lateral boundaries. WRF has been widely used as a dynamical downscaling method for future climate projection at small and regional scales (Bao et al., 2015; Brewer and Mass, 2016; Kong et al., 2019). Kong et al. (2019) found that WRF was satisfactory in reproducing spatiotemporal distribution and trends of extreme climate indices for China.

Geoengineering via increasing planetary albedo as a method of avoiding the worst excesses of climate heating has been actively discussed in climate research for well over a decade (Shepherd, 2009). The most widely studied albedo modification type (e.g. Lenton et al., 2009; Robock et al., 2009) is via stratospheric aerosol injection (SAI). To standardise and aid the evaluation of SAI in ESM simulations, Kravitz et al. (2011) proposed the Geoengineering Model Intercomparison Project (GeoMIP), with Phase 1 including two different SAI scenarios using sulfates as the aerosol and with greenhouse gas emissions from the Representative Concentration Pathway (RCP) 4.5 scenario. The impacts of SAI on temperature (Schmidt et al., 2012), precipitation (Tilmes et al., 2013) and the cryosphere (Moore et al., 2019) have been widely studied. They found that the global mean temperatures are indeed reduced but along with some regional inequalities, such as relative over-cooling of the tropics and under-cooling of the polar regions. The impacts on precipitation are relatively modest, especially compared with the less mitigated greenhouse gas scenarios. Several studies have considered global-scale impacts on temperature and precipitation extremes under both SAI and other geoengineering types designed to enhance planetary albedo (Curry et al., 2014; Aswathy et al., 2015; Ji et al., 2018), and some studies have focused on regional impacts such as in Europe (Jones et al., 2018), East Asia (Kim et al., 2020) or the Maritime Continent (Kuswanto et al., 2021).

Statistical downscaling has often been used as an alternative to dynamical methods, avoiding the significant computing resources needed to run models such as WRF. Statistical downscaling is based on the relationships found historically between ESM output and observed climate variables and is very widely used in regional impact studies (Wilby et al., 2004). All models produce results with a bias from observations, and future simulations require either bias correction or results to be shown as climate anomalies relative to some control scenario. The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP,, last access: 11 November 2022) consortium has produced methods (Hempel et al., 2013) widely used to correct the bias from CMIP5 (Climate Model Intercomparison Project phase 5) and GeoMIP outputs (McSweeney et al., 2016; Moore et al., 2019; Kuswanto et al., 2021). In our paper, we compare ISIMIP statistical downscaling methods and output from WRF dynamical downscaling and assess their performance for simulating the climate condition in the provinces around Beijing under SAI.

Figure 1(a) Map of East Asia with Chinese provincial boundaries marked in black. The 10 km WRF domain (red box) is nested inside the 30 km resolution domain (large black sector), which is centred on 116 E, 40 N on a Lambert projection. (b) The topography and primary roads (grey curves) of the 10 km resolution domain from panel (a). The provincial boundaries are marked in black with the heavier line demarking the Beijing region. The major metropolitan centres of Beijing, Tianjin, Chengde and Langfang are marked in red.

The greater Beijing region lies in complex terrain, surrounded by hills and mountains on three sides, with a flat plain to the southeast coast (Fig. 1). We explore the effect of geoengineering on surface temperature, wind and humidity in this domain. We nest a 10 km resolution domain inside a much larger 30 km resolution domain, driven at the boundaries with ESM output. We use the WRF model to dynamically downscale three time slices: 1979–1989, 2007–2017 and 2059–2069, driven by four ESMs simulating the historical, RCP 4.5, RCP8.5 and the GeoMIP G4 scenarios (Table 1). The 10 km resolution we use is not designed to study urban processes. Instead, we examine differences in downscaling at resolutions higher than, but comparable with, statistical downscaling methods that are likely to continue to be used in most geoengineering studies globally. To the best of our knowledge, this paper is the first to make dynamic downscaling of geoengineering scenarios.

Table 1The simulations in our study.

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We firstly show the differences between statistical downscaling with bias correction and dynamical downscaling without bias correction in the 1979–1989 period. This will show that a statistical downscaled and bias-corrected result, by design, has closer agreement with observations, despite its absence of physics, than the dynamically downscaled simulation. For the recent past period 2007–2017 we use the quantile delta mapping method to statistically correct the bias of the WRF simulation and assess its performance. Finally, we show the projections in the future period 2059–2069 for the greater Beijing region where global temperature differences under different greenhouse gas and G4 scenarios are known to be large. The paper is organised as follows: Sect. 2 describes the WRF model setup and parameterisation, statistical downscaling and bias-correction methods. The results from the historical simulation and future projections on the surface temperature, humidity, and wind speed are all given in Sect. 3. Finally, a summary of the main findings and the conclusion is given in Sect. 4.

2 Data and method

2.1 ESMs and scenarios (data description)

We focus on exploring the effect of SAI on surface meteorological conditions (temperature, humidity and wind) over the domain using four different dynamically and statistically downscaled ESMs. In the simulations, we use three different scenarios: RCP4.5 and RCP8.5 and the GeoMIP G4 scenario. RCP4.5 is a scenario that never exceeds a radiative forcing of 4.5 W m−2 (Thomson et al., 2011), while RCP8.5 is an unmitigated emissions scenario leading to a radiative forcing of 8.5 W m−2 at the end of the 21st century (Riahi et al., 2011). The GeoMIP experiment G4 specifies injection of sulfur dioxide into the equatorial lower stratosphere at a rate of 5 Mt yr−1 from 2020 to 2069 (Kravitz et al., 2011). These scenarios span a useful range of climate scenarios: RCP4.5 is similar (Vandyck et al., 2016) to the expected trajectory of emissions under the 2015 Paris climate accord Nationally Determined Contributions (NDCs); RCP8.5 represents a formerly business-as-usual scenario that still provides a large signal-to-noise ratio “worst case” scenario; G4 represents a similar radiative forcing as a quarter of the 1991 Mount Pinatubo volcanic eruption every year. If SAI were ever done then it would certainly use a much more sophisticated injection procedure than G4, perhaps designed to maintain hemispheric temperature balance and preserve pole–Equator temperature gradients (Macmartin and Kravitz, 2016). However, G4 levels of SAI are within the linear response of temperature reduction to material injected and within a range of radiative forcing that might be plausible or reasonable to consider (Niemeier and Timmreck, 2015). GeoMIP has also developed new experiments for use with CMIP6 level ESMs (Kravitz et al., 2015).

ESM data required as input data for WRF include meteorological fields, land surface and soil properties: specific humidity, air temperature, eastward wind, northward wind, near surface air pressure and the elevations of 30 pressure levels from 1000 to 30 hPa, soil temperatures, humidities and water contents, and sea level pressures. Only four ESMs can meet the data requirements (Table 2). We only use one single realisation (r1i1p1 using the CMIP5 nomenclature) for each model since all downscaling runs are extremely computationally expensive and some of the models have only a single realisation available.

Table 2ESMs used in this study.

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Here we use ERA5 reanalysis data as our reference. This fifth-generation ECMWF atmospheric reanalysis of global climate combining a huge number of historical observations into global estimates by advanced modelling and data assimilation systems (Hersbach et al., 2020) has been widely used for meteorological data analysis (Chen et al., 2021; Huo et al., 2022; Lee et al., 2022; Zhang et al., 2022). The performance of ERA5 for temperature (Gong et al., 2022), relative humidity (Zhang et al., 2021) and wind speed (Yu et al., 2019) analysed over China suggests it well reproduces the observed meteorological data in climatology and interannual variations. We also use the 31 km resolution ERA5 6 h reanalysis data during 1 January 1979–31 December 1989 to correct ESM climate fields at the domain boundaries as required by WRF (Hersbach et al., 2018). ERA5 reanalysis near-surface meteorological elements (2 m temperature, 2 m humidity and 10 m wind speed) are significantly correlated with observations over the area (Meng et al., 2018). We use daily temperature, humidity and wind from ERA5 for the period 1980–1989 and 2008–2017 to statistically bias correct the ESMs variables and assess the performance of WRF downscaling.

2.2 WRF

The WRF model adopts a compressible non-hydrostatic equilibrium equation and a variety of physical parameterisation schemes and data assimilation which can realise high-resolution weather forecasts at various scales (Michalakes et al., 2001). Xu et al. (2012) used WRF to improve the bias in global climate model simulations of extreme weather events. WRF needs 6 h input data, which do not exist for most variables in the ESMs climate simulation output. So, we use the available monthly ESM data to estimate 6 h input data with a pseudo global warming downscaling method (PGW-DS, Kawase et al., 2009) using ERA5 data bilinearly interpolated to the same grid as the ESM output:

(1) M f = R h - M h + M f ,

where Mf is the ESM-driven WRF model 6 h input data and Rh is 6 h data from ERA5 reanalysis during the historical period 1979–1989. For the period 1979–1989, Mh is the monthly data of ERA5 and Mf is the monthly data of ESMs under the historical scenario. For the period 2007–2017, Mh is the monthly data of ESMs during 1979–1989 under the historical scenario, and Mf takes the monthly data of ESMs during 2007–2017 under RCP4.5. For the period 2059–2069, Mh is the monthly data of ESMs during 1979–1989 under historical scenario and Mf takes the monthly data of ESMs during 2059–2069 under RCP4.5, RCP8.5 and G4 scenarios.

This study uses WRF version 3.9.1 with two nested domains, where the inner domain (longitude × latitude: 45×33) has a resolution of 10 km and the outer domain (80×58) has a resolution of 30 km (Fig. 1). The model has 30 vertical layers from the surface to 50 hPa. The integration time step is 3 min. We set the parameterisations following a study on the numerical simulation of urbanisation on regional climate in China (Wang et al., 2012) as follows: the WRF Single Moment 6-class (WSM6, Hong et al. 2006), the new version of the rapid radiative transfer model (RRTMG, Iacono et al., 2008) for both the long-wave and short-wave radiation, the MM5 similarity surface layer scheme (Paulson et al., 1970), the Yonsei University (YSU) planetary boundary layer (PBL) scheme (Noh et al., 2003), the Kain–Fritsch scheme for atmospheric convection (Kain, 2004), and the Noah land surface model (Chen and Dudhia, 2001).

Figure 2The WRF flow chart. PGW refers to the pseudo global warming downscaling method, and CDOs refer to Climate Data Operators used for generating the WRF recognisable “GRIB” format input data. WPS is the WRF Preprocessing System.


Putting the ESMs data as initial and boundary conditions into the WRF Preprocessing System (WPS; Fig. 2) is challenging. We downloaded all the required monthly data (see Table S1) from four ESMs in the three periods (1979–1989, 2007–2017, 2059–2069) and 6 h historical data from ERA5 (1979–1989). Then, we used the PGW method to get the 6 h input data during three periods (see Eq. 1). We then used Climate Data Operators (CDOs) to convert the input data in NC format files into GRIB format files that WPS can recognise. In all three simulated periods, the initial year is considered as spin-up time and is not included in our analysis.

2.3 ISIMIP statistical downscaling and bias correction

This method corrects daily variability on the premise that the monthly trend of the modelled variable is unchanged (Hempel et al., 2013). Here, we take the data from one grid point and for some single months as an example to illustrate the procedure. It includes three steps.

In step 1, we firstly bilinearly interpolate the model data to the same grid points of reanalysis data before bias correction.

In step 2, monthly bias-corrected data are found by a multi-year averaged difference between the model output and reanalysis data in our referenced period:

(2) M m * = R m - M m + M m .

The Mm* is the bias-corrected monthly data; Rm and Mm are the multi-year averaged values in this month from reanalysis data and model data during the reference period, respectively. Mm is the modelled monthly data. The subscript m represents monthly. In this step, ISIMIP does not correct the daily variability of modelled data.

In step 3, we correct the modelled daily variability to a linear regression residual:

(3) Δ M d * = B ¯ × ( M d - M m ) .

The ΔMd* is the bias-corrected residual daily data from model. Md is the modelled daily data. The subscript d represents daily. (MdMm) represents the modelled daily residual values in this month, and the residual of reanalysis data can be obtained in the same way. B¯ is the linear regression coefficient of daily residual values between reanalysis data and model data during our referenced period. Then, we can get the bias-corrected modelled daily data:

(4) M d * = M m * + Δ M d * .

The Md* is the bias-corrected daily data of model. Therefore, ISIMIP corrects the monthly mean and its daily variability. Here, we use the ERA5 reanalysis data as reanalysis data in our study. For convenience we use the term ISIMIP-ESM to denote the output from the ESMs after applying the ISIMIP statistical downscaling and bias-correction methodology.

2.4 Quantile mapping (QM) and quantile delta mapping (QDM)

Quantile mapping has been widely used as a statistical bias-correction and downscaling method (Li and Babovic, 2019; Kuswanto et al., 2021), and annual and monthly biases of all variables can be reduced to nearly zero (Wilcke et al., 2013). As a bias-correction method, quantile mapping can reproduce the frequency of different types of extreme heat wave events well (Schoof et al., 2019). Here we use the empirical cumulative distribution function (CDF) to correct the biases:

(5) M d * = F R - 1 ( F H ( M d ) ) ,

where Md* is the daily data after bias correction, F is the cumulative distribution function (CDF), F−1 is the inverse, subscript R represents the ERA5 reanalysis data, subscript H represents historical simulation results and Md is daily model output in the historical simulations. This method keeps the model and observational data CDFs as consistent as possible.

QDM is similar to QM but is non-stationary. It considers the time variability between the historical simulation and future projection; hence it is preferable for our task here (Salvi et al., 2011):

(6) M d * = F R - 1 F F M d + M d - F H - 1 F F M d .

The Md* and Md are bias-corrected and raw daily model outputs in the future simulations. The subscript R represents the ERA5 reanalysis data; the subscripts F and H represent model outputs from future and historical simulations, respectively. To preserve the spatial information of the high-resolution WRF model result, we do bias correction on daily value averaged in the whole inner domain (Fig. 1b) rather than separately for each grid point.

For the WRF simulations during 2007–2017 and 2059–2069, we use the QDM method to correct biases. Similar to ISIMIP-ESM, we use the terms WRF-ESM and WRF-QDM-ESM to represent results of WRF driven by the ESM and WRF driven by ESM after QDM bias correction, respectively.

Figure 3The region containing the high-resolution WRF domain (red boundary in right column maps), with city boundaries marked in black. The spatial distribution of mean 2 m temperature (a–d), relative humidity (e–h) and 10 m wind speed (i–l) from ERA5, downscaled ESM ensemble mean before any bias correction, ISIMIP-ESM ensemble mean and the WRF-ESM ensemble mean in the high-resolution domain (Fig. 1) during 1980–1989. The results for the four ESMs are shown separately in Figs. S1–S3 in the Supplement along with the bias-corrected versions.

Figure 4The probability density function (pdf) for daily mean temperature, relative humidity and wind speed for MIROC-ESM (a–c), MIROC-ESM-CHEM (d–f), HadGEM2-ES (g–i) and BNU-ESM (j–l) under WRF (red lines) and ISIMIP statistical bias-correction method (blue lines) in the Beijing province (Fig. 1) during 1980–1989. The black line is ERA5 reanalysis data. Values of humidity exceeding 100 % can occur with ISIMIP downscaling.


3 Results

3.1 Historical simulation: WRF and ISIMIP downscaling comparison

We compare the simulations of mean temperature, relative humidity and wind speed from raw ESM output downscaled to ERA5 resolution, ISIMIP-ESM and WRF-ESM in Beijing during the 1980s in Fig. 3. Figure 3a shows the annually averaged 2 m ERA5 temperatures in Beijing, with a mean of 7 C and highs in the southeast (12 C) and lows in the northwest, which correlates with the topography (Fig. 1). Relative humidity (Fig. 3e) varies between 50 %–55 %, with the city centre a little drier than the suburbs. Similarly, wind speed is low in the city centre with highs in the higher north western hills and south eastern plain (Fig. 3i).

The temperatures from the raw ESM outputs in Beijing have less range than both ISIMIP-ESM and WRF-ESM results due to their coarser resolution and obvious bias. The mean temperatures of MIROC-ESM and MIROC-ESM-CHEM over the domain are about 8 C, while HadGEM2-ES and BNU-ESM are cooler, and all ESM have a large cold bias compared with ERA5 (Fig. S1). ISIMIP-ESM forces the model mean data to agree with ERA5 mean observations by design and also downscales the ESM data to the observational resolution (Fig. 3c, g, k). The resulting ISIMIP-ESM means are indistinguishable by eye from the ERA5 mean in Fig. 3, though the ESM trends over time are preserved and the seasonality and other measures of variability are ESM-dependent and differ from ERA5.

WRF has a finer resolution than ERA5 and clearly shows higher temperatures in the centre of Beijing with cool temperatures over the mountains. WRF temperatures driven by MIROC-ESM and MIROC-ESM-CHEM are higher in the Beijing and Tianjin city centres than ERA5, ISIMIP-MIROC and ISIMIP-MIROC-CHEM outputs, while the temperatures in the suburbs are a little lower than from ERA5 and ISIMIP-ESM. Temperatures from HadGEM2-ES-driven WRF are a little colder than that of ERA5 and ISIMIP-HadGEM2 over Beijing (Fig. S1). WRF also produces lower relative humidity in the centre of Beijing and higher humidity in the north and west of city, consistent with the pattern of temperatures. Humidity under WRF tends to be lower in the urban centre (45 %) and higher in the suburban areas (60 %) than ISIMIP and ERA5. Relative humidities of all ESMs are higher than ERA5 (Fig. S2). The humidities of WRF under different ESMs are noticeably different from each other, although MIROC-ESM and MIROC-ESM-CHEM are similar (Fig. S2). Wind speeds in all ESMs are greater than ERA5, except HadGEM2-ES (Fig. S3). ISIMIP reduces all ESMs to essentially the ERA5 pattern as with temperature and humidity. WRF winds for all four ESMs are greatly overestimated. All WRF simulations have spatial patterns very different from ERA5, with maximums associated with the northern and western higher ground.

Figure 5Taylor diagram for daily temperature (a), wind speed (b) and relative humidity (c) of four ESMs using two downscaling methods, i.e. ISIMIP (red) and WRF (brown) compared to ERA5 data during 1980–1989 in Beijing. The blue symbols are the data from raw ESMs. The skill of downscaling methods is reflected by the distance from each symbol to the point labelled “ERA5”, the ERA5 reanalysis data. The blue lines are the correlation coefficient which represents the similarity between each downscaling data and reanalysis data. The green contours are root mean standard deviation (RMSD), and black contours are standard deviation.


The temperature, relative humidity and wind speed distributions (Fig. 4) illustrate bias and across-ESM differences for the WRF simulations. Both MIROC-ESM and MIROC-ESM-CHEM overestimate the probability of high temperatures. ISIMIP-HadGEM2 overestimates the likelihood of high temperatures when compared with ERA5, while ISIMIP-BNU overestimates both high and low temperature extremes. WRF performs well for all four ESMs compared with ISIMIP, which produces unphysical relative humidities exceeding 100 % for HadGEM2-ES and BNU-ESM. ISIMIP winds for all four ESMs tend to increase the frequency of low winds and winds exceeding 5 m s−1. WRF winds are close to twice that of ERA5. Overall, results after ISIMIP show closer mean values to ERA5, while the pdf's for WRF are closer to ERA5, but the differences between ISIMIP and WRF are small except for wind speed.

Figure 5 shows the Taylor diagrams (Taylor, 2001), which can be used to assess the skill of two downscaling methods applied to meteorological data. Temperatures from raw MIROC-ESM and MIROC-ESM-CHEM output show better performance than the other two models. WRF has a better correlation coefficient (>0.95) than ISIMIP and smaller RMSD for all four ESMs. Wind speed of all four ESM outputs have correlation coefficients <0.1 with ERA5. ISIMIP greatly reduces errors and variance (except for HadGEM2-ES) but does not improve correlation. WRF has better correlation, has lower errors and shows better skill on simulating wind speed than ISIMIP, despite its systematic bias in magnitude.

Figure 6The spatial distribution of mean 2 m temperature (a–c), relative humidity (d–f) and 10 m wind speed (g–i) from ERA5, WRF-ESM and WRF-QDM-ESM ensemble mean during 2008–2017. Figures S4–S6 show the four ESM results separately.

3.2 Bias correction for WRF

We use QDM to correct biases of the WRF results for the 2008–2017 historical simulation. The temperature, relative humidity and wind speed from ERA5 over Beijing during 2008–2017 (Fig. 6) have a very similar pattern with those during the 1980s (Fig. 3). Average temperatures slightly increased over Beijing when compared with the 1980s. Average humidities in most places during 2008–2017 are slightly higher than during the 1980s, while in the northwest of Zhangjiakou, where temperatures rose fastest, humidity shows a slight decrease (Figs. 3 and 6). Winds in Beijing between these 2 decades did not change.

Figure 7Seasonal cycle of multi-year averaged monthly temperature (a), relative humidity (b) and wind speed (c) during 2008–2017 for Beijing. The solid lines are the WRF-QDM-ESM results, and the dashed lines are the WRF-ESM results.


WRF-QDM-ESM simulations of the three variables (Fig. 6) exhibit geographic patterns that are the same as those during the 1980s (Fig. 3). QDM bias correction makes the temperature hotter and the humidity drier especially in high mountains and cities, producing spatial patterns closer to ERA5 (Fig. 6). QDM bias correction greatly improves wind speed results from uncorrected values of 4–5 m s−1 to 1.5–2.5 m s−1 across most areas. The mean 2 m temperature of WRF-QDM-HadGEM is hotter than the other three ESMs (16 C in the centre of Beijing), while the other three reach only 14 C, which is close to ERA5 (Fig. S4e–h). WRF-QDM-MIROC and WRF-QDM-MIROC-CHEM are more humid than WRF-QDM-HadGEM2 and WRF-QDM-BNU. The wind speed of WRF-QDM-BNU is a little slower than other three ESMs.

The seasonal cycle of average daily temperature simulated by WRF is close to ERA5 (Fig. 7). However, the temperature of WRF-QDM-BNU shows a colder bias than the raw results in the summer, and the temperature of WRF-QDM-HadGEM2 shows a warmer bias than the results from WRF-HadGEM2 in the winter. For humidity, the overall performance of WRF-QDM-ESM is not good, and they all show a dry bias relative to ERA5 from July to the following May. After bias correction, the wind speeds from all ESMs clearly decrease to the same range as ERA5. Winds from HadGEM2-ES show the best agreement in both the quantity and seasonality with ERA5, but QDM does not change the seasonality of wind speed, just its amplitude.

Figure 8Taylor diagram for daily temperature (a), relative humidity (b) and wind speed (c) of WRF driven by four ESMs results with QDM bias correction (red) and without bias correction (blue) compared to ERA5 reanalysis data (purple star) during 2008–2017 in Beijing.


Figure 9The spatial distribution of ensemble mean 2 m temperature (a–f), relative humidity (g–l) and 10 m wind speed (m–r) under G4, RCP4.5 and RCP8.5 scenarios based on ISIMIP-ESM and WRF-QDM-ESM results during 2060–2069.

The skill of QDM for correcting daily temperature, relative humidity and wind from the biased WRF-ESM are shown in Fig. 8. QDM changes the errors but has little effect on the correlation with the ERA5 data. While temperature and wind speed all improve with QDM, humidities from WRF-QDM-ESM have a lower performance than does the corresponding WRF-ESM results except for WRF-QDM-HadGEM2. We regard bias correcting as necessary for WRF outputs.

3.3 Future projections

We now look at temperature, humidity and wind projections for 2060–2069. Figure 9 shows maps of ensemble mean 2 m temperature (Fig. 9a–f), relative humidity (Fig. 9g–l) and 10 m wind speed (Fig. 9m–r) under the G4, RCP4.5 and RCP8.5 scenarios. Mean temperatures over the domain are 12.5, 13.3 and 14.8 C from ISIMIP-ESM and 13.1, 13.8 and 15.2 C from WRF-QDM-ESM under G4, RCP4.5 and RCP8.5 scenarios, respectively. The higher city centre temperatures from WRF-QDM-ESM account for the 0.5 C difference from ISIMIP-ESM. Both ISIMIP-ESM and WRF-QDM-ESM produce similar overall temperature patterns driven by topography. Relative humidities under geoengineering and RCP scenarios are almost the same. Mean model relative humidities are 53.8 %, 53.6 % and 53.4 % by ISIMIP-ESM, and slightly wetter than 50.0 %, 49.7 % and 50.2 % from WRF-QDM-ESM, under G4, RCP4.5 and RCP8.5 scenarios respectively. This is mainly due to lower humidities with WRF-QDM-ESM in city centres. Wind spatial patterns are clearly different from ISIMIP-ESM and WRF-QDM-ESM. The wind speed in the southwest of the domain from ISIMIP-ESM is low, while WRF-QDM-ESM winds are lowest in the city centre. Although there are some differences in details between different ESMs, the overall results are similar to those of ensemble means (Figs. S7–S12).

Figure 10 shows temperature, humidity and wind anomalies from WRF-QDM-ESM and ISIMIP-ESM simulations. The mean temperature in the 2060s under G4 is 1–2 C higher than that during 2008–2017. Temperatures from both ISIMIP-ESM and WRF-QDM-ESM are cooler over the whole domain under G4 than those under RCP4.5 by 0.1–1.5 C, while there is a larger cooling effect of 1.6–2.8 C under G4 relative to RCP8.5 (Table 3). There is large across-model spread with the two MIROC models having smaller differences (G4-RCP4.5) than the other two models (Table 3), and the two MIROC models show larger differences from each other with ISIMIP-ESM than with WRF-QDM-ESM. Relative humidity anomalies exhibit large differences under different scenarios for ISIMIP-ESM and WRF-QDM-ESM. G4 humidity from ISIMIP-ESM shows a slight reduction of 1 percentage point relative to the 2010s over Beijing, while there is an increment of similar magnitude from the WRF-QDM-ESM results. When compared to RCP4.5 scenario, the humidity under G4 from ISIMIP-ESM shows a slight (1 percentage point) increase, but that from WRF-QDM-ESM shows no statistically significant change. The differences of relative humidities from WRF-QDM-ESM and ISIMIP-ESM between G4 and RCP8.5 show opposite trends, although differences are slight. ISIMIP-ESM winds under G4 are a little slower than those during the 2010s and show no significant difference between G4 and RCP4.5. Compared to RCP8.5, G4 winds from ISIMIP-ESM increase by 0.15 m s−1 mainly in the south of the domain. Winds from WRF-QDM-ESM show very small changes with slight increases relative to the RCP scenarios. Humidity anomalies from ISIMIP have a difference under G4 relative to RCP8.5 in the southwest of the domain, where wind speed anomalies show an obvious positive change, while for WRF there are no particular patterns.

Table 3Difference of 2 m temperature between G4 and ERA5 in the 2010s and RCP scenarios in the 2060s for the high-resolution domain (Fig. 1). Bold indicates the differences or changes are significant at the 95 % confidence level according to the Wilcoxon signed rank test (units: C).

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Figure 10Spatial pattern of ensemble mean 2 m temperature (a–f), relative humidity (g–l) and 10 m wind (m–r) scenario differences: G4-2010s (left column), G4-RCP4.5 (middle column) and G4-RCP8.5 (right column) based on ISIMIP-ESM and WRF-QDM-ESM results. 2010s means the results simulated during 2008–2017, and G4, RCP4.5 and RCP8.5 mean the results projected during 2060–2069. Stippling indicates grid points where differences are not significant at the 95 % confidence level according to the Wilcoxon signed rank test. Figures  S13–S18 show the results for each ISIMIP-ESM and WRF-QDM-ESM separately.

4 Discussion and conclusions

We have explored the impact of geoengineering on surface temperature, humidity and wind speed over Beijing during 2060–2069 using statistical bias correction and dynamical downscaling. We evaluated the performance of ISIMIP and WRF methods during 1980–1989 based on the historical simulations from four ESMs. WRF output needs to be bias corrected for it to be comparable with observations or statistically downscaled and bias-corrected output. We use the QDM method to correct the bias in the WRF results since QDM ensures that the pdf of simulation results is consistent with the reanalysis data. Because we want to keep the high spatial resolution of the WRF model simulation, we do not correct biases grid cell by grid cell, which would produce output at the reanalysis resolution, but instead do bias correction for daily mean temperature, humidity and wind speed over the domain.

The raw output from the ESMs of temperature, humidity and wind speed have no clear spatial distribution over the domain because cities occupy only a few ESM grid cells. Statistical bias correction and downscaling by the ISIMIP method produces output at the same resolution as the observational reanalysis data and matches its spatial distribution. The ISIMIP method is designed to preserve trends and the long-term mean value the same as the observations (Hempel et al., 2013). Dynamically downscaling demands a higher-resolution grid, and WRF produces an output at a spatial resolution independent of the resolution of the reanalysis data. The WRF results not only produce characteristics consistent with the reanalysis data, but also depict the more detailed meteorological characteristics created by the complex underlying land surface that are input to WRF. The WRF simulation during 1980–1989 showed higher temperatures in the city centres than those in the suburbs and lower temperatures in the western and northern mountainous areas. This pattern is created by the joint actions of latitude, terrain and underlying surface. The pattern of relative humidity distribution is anticorrelated to temperature, with lower humidity in the urban centre, while humidities in some mountainous areas are higher. This is similar to the pattern of humidity from 42 AWSs (automatic weather stations) in Beijing during 2007–2015 (Yang et al., 2017) and due mainly to the underlying surface and the transpiration of the urban area being less than that of the suburbs (Dou et al., 2020). Wind speeds inside the Beijing urban area are low but reach a maximum in its western foothills. Urbanisation increases surface roughness, lowering wind speed (Liu et al., 2020). Correlation coefficients of wind speed between the WRF downscaling results and ERA5 for all four ESM raw results are all <0.1. Zha et al. (2020) and Jiang et al. (2017) also found similar low correlations. Zha et al. (2020) project the near-surface wind speed over eastern China based on a CMIP5 dataset and found that 18 of the 24 ESMs analysed show negative correlations with observed wind speed during 1979–2005. The low correlations are to be expected when considering variability in simulated weather at high temporal resolution. Jiang et al. (2017) found that differences in CMIP5 model wind responses to the East Asian monsoon in China are related to model parameterisation and horizontal resolution. The pdf of temperature suggests that the WRF result is more realistic and closer to observations than results from ISIMIP, although the MIROC-ESM and MIROC-ESM-CHEM show a higher probability for high temperatures. The pdf of humidity strongly indicates that WRF performs better than ISIMIP. But WRF tends to overestimate the wind speed even though the shape of pdf is more similar to observations than that of ISIMIP output. Many studies show that WRF frequently overestimates wind speed, e.g. in the Gulf of Mexico (Lee et al., 2011), in the coastal cities of Spain (Chen et al., 2012) and in south eastern Texas (Ngan et al., 2013), because of imperfect surface representation (e.g. urban vegetation and surface morphology). Overestimated surface wind speed in WRF is caused by using smoothed topography in the model (Jimenez and Dudhia, 2012). Overall, for temperature and humidity, the results of WRF are better than those of ISIMIP, and both are better than the original ESM output. WRF has better correlation with observations than ISIMIP.

Applying QDM bias correction to WRF reduces model and monthly dependent differences. Temperatures, both before and after bias correction, have high correlations with ERA5, and QDM makes little difference. WRF relative humidity, however, is always drier than that of ERA5 in winter whether revised or not. Wind speeds are lowered after bias correction to the same levels as ERA5, but QDM does not have a clear effect on correlation (Zhao et al., 2017).

The spatial distribution of temperature, humidity and wind speed are roughly similar in all three periods assessed, that is the 1980s, 2010s and 2060s, whether from ISIMIP-ESM or WRF-QDM-ESM. Our analysis shows that mean temperature under G4 is always lower than that under RCP4.5 and RCP8.5. Although it does not return temperatures to the historical level, that was not the design of the experiments, which instead simply explore the effects of injecting roughly 14 the amount of SO2 into the equatorial lower stratosphere as the 1991 Mount Pinatubo eruption every year for 50 years. Using ISIMIP downscaling leads to larger differences between scenarios than using WRF. HadGEM2-ES shows the largest difference in temperatures between G4 and RCP scenarios of the four ESMs we studied. For the relative humidity, ISIMIP-ESM and WRF-QDM-ESM give opposite (but small) signed anomalies between G4 and RCP4.5 in our domain.

This paper is the first to use WRF for regional dynamic downscaling of geoengineered climates and impacts on relatively small spatial and temporal scales, which can be useful for regions that need higher resolutions than ERA5, and statistical downscaling can supply. The differences between statistical downscaling and dynamic downscaling in the Beijing region, that extends from sea level to mountains about 2000 m in elevation, may appear rather modest. But even these modest differences in derived temperatures and humidities can make for large differences in compound indices such as apparent temperature, particularly when assessing future risk to urban populations.

Code and data availability

All ESM data used in this work are available from the Earth System Grid Federation (; WCRP, 2021). The WRF and ISIMIP bias-corrected and downscaled results are available from the authors on request. The WRF and ISIMIP codes are freely available at the references cited in the methods sections.


The supplement related to this article is available online at:

Author contributions

JCM and LZ designed the experiments, and JW performed the simulations. JW and JCM prepared the article with contributions from all co-authors.

Competing interests

The contact author has declared that none of the authors has any competing interests.


Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Special issue statement

This article is part of the special issue “Resolving uncertainties in solar geoengineering through multi-model and large-ensemble simulations (ACP/ESD inter-journal SI)”. It is not associated with a conference.


We thank the climate modelling groups for participating in the Geoengineering Model Intercomparison Project and their model development teams, the CLIVAR/WCRP Working Group on Coupled Modelling for endorsing the GeoMIP, and the scientists managing the Earth system grid data nodes who have assisted with making GeoMIP output available. Ben Kravitz provided useful advice on the article.

Financial support

This research was funded by the National Key Science Program for Global Change Research (grant no. 2015CB953602).

Review statement

This paper was edited by Ben Kravitz and reviewed by two anonymous referees.


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Short summary
We examine how geoengineering using aerosols in the atmosphere might impact urban climate in the greater Beijing region containing over 50 million people. Climate models have too coarse resolutions to resolve regional variations well, so we compare two workarounds for this – an expensive physical model and a cheaper statistical method. The statistical method generally gives a reasonable representation of climate and has limited resolution and a different seasonality from the physical model.
Final-revised paper