Stratospheric Assimilation, Weather Forecast, and Climate Prediction Model Based on Data Assimilation Research Testbed and Whole Atmosphere Community Climate Model
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摘要: 本论文基于WACCM（Whole Atmosphere Community Climate Model）模式最新版本WACCM6和DART（Data Assimilation Research TestBed）同化工具最新版本Manhattan，开发了中高层大气温度、臭氧和水汽卫星资料的同化接口，搭建了一个包含完整平流层过程的数值同化、天气预报和短期气候预测模型（此后简称模型）；本模型对2020年3～4月平流层大气变化进行了同化观测资料的模拟，并以同化试验输出的分析场作为初值，对5～6月的平流层大气进行了0～30天天气尺度预报以及31～60天短期气候尺度预测的回报试验。结果表明：本模型能较好地重现2020年3、4月北极平流层出现的大规模臭氧损耗事件随时间的演变特征，模拟结果和Microwave Limb Sounder（MLS）卫星观测结果很接近；而未进行同化的模拟试验，虽然可以模拟出北极臭氧损耗现象，但是模拟的臭氧损耗规模相比MLS卫星观测结果要低很多；利用同化试验4月末输出的分析场作为初值，预报的5月北极平流层臭氧体积混合比变化与MLS卫星观测值的差值小于0.5，预测的6月北极平流层臭氧变化只在10～30 hPa之间的区域，与观测之间的差异达到了1 ppm（ppm=10−6）。本模型不但改善了北极平流层化学成分变化的模拟，也显著地提升了北极平流层温度和环流的模拟。本模型同化模拟的3～4月、预报预测的5～6月北极平流层温度和纬向风变化与Modern-Era Retrospective analysis for Research and Applications, Version 2 （MERRA2）再分析资料结果具有很好的一致性，仅在北极平流层顶部，预报预测的温度和纬向风分别与再分析资料之间的均方根误差（RMSE）约为3 K和4 m s−1。未进行同化的试验模拟的3～4月、预报预测的5～6月北极平流层的温度和纬向风与MERRA2再分析资料之间的RMSE在大部分区域都达到6 K及5 m s−1以上。从全球范围来看，本模型对平流层中低层模拟性能改善最为显著，其预报预测结果与观测值之间的差异，比未进行同化试验的结果，减少了50%以上。Abstract: Herein, an assimilation interface for temperature, ozone, and water vapor data in the middle and upper atmosphere is developed, and an assimilation model with complete stratospheric processes, including assimilation, weather forecasting, and short-term climate prediction system, is built based on the latest version of the whole atmosphere community climate model (WACCM6) and the data assimilation research testbed. Using the assimilation analysis field as the initial value, the system performed assimilation simulations of the stratospheric atmosphere in March and April 2020 and provided 0–3 days, 4–15 days, and 16–30 days forecast, as well as 1–60 days short-term climate prediction for stratospheric atmosphere changes in May and June. The results show that the system can accurately reflect the time evolution of the very unusual ozone depletion event in the Arctic stratosphere in March and April 2020, which is very similar to the microwave limb sounder (MLS) satellite observations. While simulations without assimilation can simulate the Arctic ozone depletion event, the ozone depletion magnitude is less than that of the MLS satellite. The assimilation model system improves the simulation of not only the chemical composition of the Arctic stratosphere but also the Arctic stratospheric temperature and circulation changes. The simulated March and April and predicted May and June Arctic stratospheric temperature and zonal wind variability are in good agreement with the assimilation and reanalysis results using Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2). The root mean square error (RMSE) between the predicted temperature and zonal wind and the reanalysis data is about 3 K and 4 m s−1, respectively, for the top of the stratosphere. The RMSE between the temperature and zonal wind of the Arctic stratosphere simulated by the experiment without assimilation in March-April and forecasted in May-June and the MERRA2 reanalysis data reach about 6 K and 5 m s−1 or more in most regions. On a global scale, this system has the most significant improvement in the simulation of the middle and lower stratosphere, with the RMSE in the prediction results reduced by more than 50% when compared to unassimilated simulation experiment prediction results.
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Key words:
- Stratosphere /
- Assimilation /
- Weather forecasting /
- Short-term climate prediction
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图 1 第一组试验（同化SABER资料的试验，见表1）模拟的2020年（a）3月1日、（b）3月15日、（c）3月30日、（d）4月15日和（e）4月30日北极臭氧柱总量（TCO，单位：DU）分布
Figure 1. First set of experiments (Table 1) and simulated total column ozone (TCO, units: DU) distributions for (a) March 1, (b) March 15, (c) March 30, (d) April 15, and (e) April 30, 2020
图 2 MERRA2再分析资料中，过去30年（1990～2020年）60oN～90oN平均的TCO（单位：DU）月变化曲线。黑线为1990～2019年TCO月变化的平均结果，阴影区域代表1990～2019年TCO的月变化范围，蓝线代表2020年TCO月变化曲线
Figure 2. MERRA2 reanalysis of monthly TCO (units: DU) changes averaged over 60°–90° N for the past 30 years (1990–2020). The black line indicates the average of the monthly TCO changes from 1990–2019, while the shaded area represents the range of TCO changes from 1990–2019. The blue line indicates the monthly TCO change for 2020
图 3 第二组试验（未进行同化的试验，见表1）模拟的2020年（a）3月1日、（b）3月15日、（c）3月30日、（d）4月15日和（e）4月30日TCO（单位：DU）分布
Figure 3. Second set of experiments (see Table 1) simulated TCO (units: DU) distributions for (a) March 1, (b) March 15, (c) March 30, (d) April 15, and (e) April 30, 2020
图 4 （a）第一组试验（同化SABER资料的试验，见表1）模拟的2020年3、4月北极地区平均的臭氧含量变化。（b）第二组试验（未进行同化的试验，见表1）模拟的2020年3、4月北极地区平均的臭氧含量变化。（c）为（a）中的臭氧含量变化与过去20年北极地区平均的臭氧变化的气候态的差值。（d）为（a）中的臭氧含量变化与MLS资料中的北极地区平均的臭氧含量变化的差值。单位：ppm（ppm=10−6）
Figure 4. (a) Simulated changes in Arctic-averaged ozone from the first set of experiments in March and April 2020 (Table 1). (b) Simulated changes in Arctic-averaged ozone from the second set of experiments in March and April 2020 (Table 1). (c) Difference between the ozone changes in (a) and Arctic-averaged ozone change over the past decade. (d) Difference between the ozone changes in (a) and the Arctic-averaged ozone changes based on MLS data. Units: ppm
图 5 （a）第三组试验（以第一组同化试验输出的分析场为初值的试验，见表1）预报的2020年5月和预测的6月北极地区平均的臭氧含量变化。（b）为（a）图中的臭氧含量变化与MLS资料中臭氧含量变化的差值。单位：ppm
Figure 5. (a) Arctic-averaged ozone changes forecasted and predicted by the third set of experiments (Table 1) for May and June 2020. (b) Difference between ozone changes in (a) and those in the MLS data. Units: ppm
图 6 （a）第一组试验（同化SABER资料的试验，见表1）模拟的2020年3、4月北极地区平均的温度变化。（b）第二组试验（未进行同化的试验，见表1）模拟的2020年3、4月北极地区平均的温度变化。（c）MERRA2再分析资料中的2020年3、4月北极地区平均的温度变化。（d）为（a）与（c）的差值。（e）为（b）与（c）的差值。单位：K
Figure 6. (a) Arctic-averaged temperature change in March and April 2020 simulated in the first set of experiments (Table 1). (b) Arctic-averaged temperature change in March and April 2020 simulated in the second set of experiments (Table 1). (c) MERRA2 reanalysis for Arctic-averaged temperature change in March and April 2020. (d) Difference between (a) and (c). (e) Difference between (b) and (c). Units: K
图 8 （a）2020年4月30日北极地区（60°～90°N）平均的温度垂直曲线。黑线基于MERRA2资料，蓝线基于第一组试验（同化SABER资料的试验）资料，红线基于第二组试验（未进行同化的试验）资料。（b）中蓝线为（a）中蓝线与黑线之差，代表第一组试验结果与MERRA2的差值，红线为（a）中红线与黑线之差，代表第二组试验结果与MERRA2的差值。（c）、（d）与（a）、（b）类似，但是为纬向风的变化
Figure 8. (a) Average temperature vertical curve for the Arctic region (60°–90° N) for April 30, 2020. The black line corresponds to MERRA2, the blue line to the first set of experiments, and the red line to the second set of experiments. (b) Difference between the first set of experiments and the MERRA2 (blue line) and difference between the second set of experiments and the MERRA2 (red line). (c) and (d) are similar to (a) and (b), except for zonal wind changes
图 9 （a）第三组试验（以第一组同化试验输出的分析场为初值的试验，见表1）预报的2020年5月和预测的6月北极地区平均的温度变化。（b）第四组试验（以第二组未进行同化的试验输出的分析场为初值的试验，见表1）预报的5月和预测6月北极地区平均的温度变化。（c）MERRA2再分析资料中的2020年5、6月北极地区平均的温度变化。（d）为（a）与（c）的差值。（e）为（b）与（c）的差值。单位：K
Figure 9. (a) Forecasted and predicted changes in Arctic-averaged temperature for May and June 2020 by the third set of experiments (Table 1). (b) Forecasted and predicted changes in Arctic-averaged temperature for May and June by the fourth set of experiments (Table 1). (c) Arctic-averaged temperature changes for May and June 2020 based on MERRA2 reanalysis data. (d) Difference between (a) and (c). (e) Difference between (b) and (c). Unis: K
图 11 （a）和（c）分别为第一组试验（同化SABER资料的试验，见表1）模拟的2020年3、4月平流层温度和纬向风与MERRA2再分析资料之间的RMSE。（b）和（d）分别为第二组试验（未进行同化的试验，见表1）模拟的2020年3、4月平流层温度和纬向风与MERRA2再分析资料之间的RMSE
Figure 11. (a) Stratospheric temperature and (c) wind RMSEs of March and April 2020 between the first set of experiments (Table 1) and the MERRA2 reanalysis data; (b) stratospheric temperature and (d) wind RMSEs of March and April 2020 between the second set of experiments (Table 1) and the MERRA2 reanalysis data
图 12 第三组试验（以第一组同化试验输出的分析场为初值的试验，见表1）输出的平流层温度2020年5月（a）0～3天预报、（c）4～15天预报和（e）16～30天预报以及（g）2020年6月短期气候预测结果与MERRA2再分析资料之间的RMSE。（b）、（d）、（f）和（h）分别为第四组试验（以第二组未进行同化的试验输出的分析场为初值的试验，见表1）输出的平流层温度2020年5月（b）0～3天预报、（d）4～15天预报和（f）16～30天预报以及（h）6月短期气候预测结果与MERRA2再分析资料之间的RMSE
Figure 12. Stratospheric temperature RMSEs between the (a) 0–3 days forecast, (c) 4–15 days forecast, (e) 16–30 days forecast, and (g) short-term climate prediction from the third set of experiments (Table 1) and the MERRA2 reanalysis data; stratospheric temperature RMSEs between the (b) 0–3 days forecast, (d) 4–15 days forecast, (f) 16–30 days forecast, and (h) short-term climate prediction from the fourth set of experiments (Table 1) and the MERRA2 reanalysis data
表 1 四组试验设计
Table 1. Design of experiments
模拟时段 同化 海温，海冰 试验初值 第一组 2020年3月1日至4月30日 SABER温度、臭氧、水汽 Hadley观测资料 WACCM6默认设置输入 第二组 2020年3月1日至4月30日 无同化 Hadley观测资料 WACCM6默认设置输入 第三组 2020年5月1日至6月30日 无同化 CFSv2海洋模式预测资料 第一组试验输出的4月30日分析场 第四组 2020年5月1日至6月30日 无同化 CFSv2海洋模式预测资料 第二组试验输出的4月30日分析场 
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