统计降尺度方法在青海省冬季最低温度预测中的应用

利用2003-2007年国家气象中心T213L31全球中期数值预报模式逐日输出产品与青海地区25个气象站的观测数据作为试验资料, 利用相关系数和逐步回归进行因子选择, 并以单隐层神经网络和多元回归作为降尺度方法进行对比研究, 用2003-2006年间的11月1日～次年3月1日的资料作为训练样本, 以数值预报产品和前一日观测的最低温度作为因子, 建立青海省25个气候站的冬季最低温度的24, 48, 72 h预报模型, 并且以2006年12月和2007年的1、 2月作为24, 48, 72 h逐日最低温度预报试验时段。试验表明, 对于青海地区来说, 青海北部地区的预报命中率总体好于南部高原地区; 在4种对比方案中, 以选择数值预报资料结合前一日地面观测的最低温度作为主要因子的方法相对较优, 随着预报时效的延长, 24 h历史实况的作用逐渐减弱; 对于所有台站来说, 这4种方案各有优缺点, 没有一种方案可以完全代替其他所有方案; 在实际业务运行中, 对不同的台站应采用不同的预报方案进行实际业务预报。     

基于MIROC/WRF嵌套模式的中国气候降尺度模拟

开展了基于嵌套的全球模式MIROC和区域气候模式WRF的动力降尺度模拟试验,检验该模式对中国气候的模拟性能,得到以下结论:全球气候模式MIROC和WRF都能较好地模拟出中国年平均地表气温(下文简称气温)分布。WRF模式对气温场的描述更为细致,模拟出了四川盆地高温和中国最北方区域的低温。两个模式总体上对南方降水模拟好于北方地区,东部地区好于西部地区。MIROC模式模拟的年平均和各季节降水与观测的 空间相关系数在0.79～0.83之间,表明它对降水的模拟较好。WRF模式模拟的降水空间分布好于MIROC模式。MIROC模式在青藏高原东南侧存在虚假降水中心,WRF能有效改进该地区降水的模拟。两个模式对年平均气温和降水年际变率的模拟能力均较差,WRF模式相对MIROC模式有一定改进。     

Future projection of East China Sea temperature by dynamic downscaling of the IPCC_AR4 CCSM3 model result

Future temperature distributions of the marginal Chinese seas are studied by dynamic downscaling of global CCSM3 IPCC_AR4 scenario runs.Different forcing fields from 2080-2099 Special Report on Emissions Scenarios(SRES) B1,A1,and A2 to 1980-1999 20C3M are averaged and superimposed on CORE2 and SODA2.2.4 data to force high-resolution regional future simulations using the Regional Ocean Modeling System(ROMS).Volume transport increments in downscaling simulation support the CCSM3 result that with a weakening subtropical gyre circulation,the Kuroshio Current in the East China Sea(ECS) is possibly strengthened under the global warming scheme.This mostly relates to local wind change,whereby the summer monsoon is strengthened and winter monsoon weakened.Future temperature fluxes and their seasonal variations are larger than in the CCSM3 result.Downscaling 100 years’ temperature increments are comparable to the CCSM3,with a minimum in B1 scenario of 1.2-2.0°C and a maximum in A2 scenario of 2.5-4.5°C.More detailed temperature distributions are shown in the downscaling simulation.Larger increments are in the Bohai Sea and middle Yellow Sea,and smaller increments near the southeast coast of China,west coast of Korea,and southern ECS.There is a reduction of advective heat north of Taiwan Island and west of Tsushima in summer,and along the southern part of the Yellow Sea warm current in winter.There is enhancement of advective heat in the northern Yellow Sea in winter,related to the delicate temperature increment distribution.At 50 meter depth,the Yellow Sea cold water mass is destroyed.Our simulations suggest that in the formation season of the cold water mass,regional temperature is higher in the future and the water remains at the bottom until next summer.In summer,the mixed layer is deeper,making it much easier for the strengthened surface heat flux to penetrate to the bottom of this water.     

Statistical downscaling of reference evapotranspiration in Haihe River Basin: applicability assessment and application to future projection

Future changes in reference evapotranspiration (ET₀) are of increasing importance in assessing the potential impacts on hydrology and water resources systems of more pronounced climate change. This study assesses the applicability of the Statistical Downscaling Model (SDSM) in projecting ET₀, and investigates the seasonal and spatial patterns of future ET₀ based on general circulation models (GCMs) across the Haihe River Basin. The results indicate that SDSM can downscale ET₀ well in term of different basin-averaged measures for the HadCM3 and CGCM3 GCMs. HadCM3 has a much superior capability in capturing inter-annual variability compared to CGCM3 and thus is chosen as the sole model to assess the changes in future ET₀. There are three homogeneous sub-regions of the Haihe River Basin: Northwest, Northeast and Southeast. Change points are detected at around 2050 and 2080 under the A2 and B2 scenarios, respectively. The Northwest is revealed to have a slight to strong increase in ET₀, while the Northeast and the Southeast tend to experience a pattern change from decrease to increase in ET₀.

EDITOR M.C. Acreman

ASSOCIATE EDITOR J. Thompson     

Recent Near-surface Temperature Trends in the Antarctic Peninsula from Observed,Reanalysis and Regional Climate Model Data

This study investigates the recent near-surface temperature trends over the Antarctic Peninsula.We make use of available surface observations,ECMWF’s ERA5 and its predecessor ERA-Interim,as well as numerical simulations,allowing us to contrast different data sources.We use hindcast simulations performed with Polar-WRF over the Antarctic Peninsula on a nested domain configuration at 45 km(PWRF-45)and 15 km(PWRF-15)spatial resolutions for the period 1991?2015.In addition,we include hindcast simulations of KNMI-RACMO21P obtained from the CORDEX-Antarctica domain(~50 km)for further comparisons.Results show that there is a marked windward warming trend except during summer.This windward warming trend is particularly notable in the autumn season and likely to be associated with the recent deepening of the Amundsen/Bellingshausen Sea low and warm advection towards the Antarctic Peninsula.On the other hand,an overall summer cooling is characterized by the strengthening of the Weddell Sea low as well as an anticyclonic trend over the Amundsen Sea accompanied by northward winds.The persistent cooling trend observed at the Larsen Ice Shelf station is not captured by ERA-Interim,whereas hindcast simulations indicate that there is a clear pattern of windward warming and leeward cooling.Furthermore,larger temporal correlations and lower differences exhibited by PWRF-15 illustrate the existence of the added value in the higher spatial resolution simulation.     

未来黄、东海营养盐浓度变化情景预测

本文基于FGOALS(Flexible Global Ocean-Atmosphere-Land System model)对未来气候情景的预测结果,结合千年生态评估的未来两个情景下的河流营养盐载荷特征,利用黄、东海水动力模型和生态模型并采用降尺度的方法对未来黄、东海营养盐的分布特征进行情景预测。结果表明,两个情景下未来河口邻近海区营养盐浓度将显著增加,富营养化加剧;GO(Global Orchestration)情景下,河流无机氮载荷增幅较大,夏季黄海中部无机氮浓度明显升高;AM(Adapting mosaic)情景下,由于河流无机磷载荷增幅较大,海区氮磷比有所下降,夏季黄海中部表层无机氮浓度降低,而在底层升高。通过敏感性实验并结合收支分析对各海区水动力条件未来变化、河流载荷变化的相对贡献进行了评估:相对于水温和水动力环境改变,河流营养盐排放量的增长是未来营养盐浓度增加的主要原因。营养盐收支分析表明,未来对流和混合输运的变化有助于黄海营养盐浓度的增加,夏季生物量升高造成更多碎屑沉降并在底层矿化使得层化季节冷水团底部营养盐浓度增长;长江口邻近海区营养盐浓度增长主要受冲淡水羽流的影响;净初级生产增加加剧了营养盐的消耗。     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

基于印度洋海温信号的我国西北地区东部夏季降水组合降尺度预测方法研究

利用国家气候中心第二代气候模式预测业务系统(BCC-CPSv2)预测产品,引入印度洋海温信号,采用组合降尺度方法建立了西北地区东部汛期降水预测模型。该预测模型对1991—2017年西北地区东部夏季降水的回报技巧较BCC-CPSv2预测技巧显著提高,空间相关系数由0.42提高到0.75,均方根误差明显减小,最多下降达80%。预测模型对降水空间分布型的预测能力较好,很好地回报了典型年份（1987年和2010年）夏季的降水距平百分率分布。通过抓住气象变量的空间分布特征,组合降尺度方法可以修正动力模式产品的预测误差,为西北地区东部夏季降水预测提供科学依据和技术支持,具有较好的应用前景。     

Using a Bayesian estimator to combine information from a cluster analysis and remote sensing data to estimate high-resolution data for agricultural production in Germany

In Germany, a county-resolution data set that consists of 35 land-use and animal-stock categories has been used extensively to assess the impact of agriculture on the environment. However, because such environmental effects as emission or nutrient surplus depend on the location, even a county resolution might produce misleading results. The aim of this article is to propose a Bayesian approach which combines two sorts of information, with one being treated as defining the prior and the other the data to form a posterior, used to estimate a data set at a municipality resolution. We define the joint prior density function based on (i) remote sensing data, thus accounting for differences in county data and missing data at the municipality level, and (ii) the results of a cluster analysis that was previously applied to the micro-census, whereas the data are defined by official statistics at the county level. This approach results in a fairly accurate data set at the municipality level. The results, using the proposed method, are validated by the national research data centre by comparing the estimates to actual observations. The test statistics presented here demonstrate that the proposed approach adequately estimates the production activities.     

Meteorology in Canada: A reaction to the rose report

The methods used in an earlier study focusing on the province of Ontario, Canada, were adapted for this current study to expand the study area over eastern Canada where the infrastructure is at risk of being impacted by freezing rain. To estimate possible impacts of climate change on future freezing rain events, a three-step process was used in the study: (1) statistical downscaling, (2) synoptic weather typing, and (3) future projections. A regression-based downscaling approach, constructed using different regression methods for different meteorological variables, was used to downscale the outputs of eight general circulation models to each of 42 hourly observing stations over eastern Canada. Using synoptic weather typing (principal components analysis, a clustering procedure, discriminant function analysis), the freezing rain-related weather types under historical climate (1958–2007) and future downscaled climate conditions (2016–2035, 2046–2065, 2081–2100) were identified for all selected stations. The potential changes in the frequency of future daily freezing rain events can be projected quantitatively by comparing future and historical frequencies of freezing rain-related weather types.

The modelled results show that eastern Canada could experience more freezing rain events late this century during the coldest months (i.e., December to February) than the averaged historical conditions. Conversely, during the warmest months of the study season (i.e., November and April in the southern regions, October in the northern regions), eastern Canada could experience less freezing rain events late this century. The increase in the number of daily freezing rain events in the future for the coldest months is projected to be progressively greater from south to north or from southwest to northeast across eastern Canada. The relative decrease in magnitude of future daily freezing rain events in the warmest months is projected to be much less than the relative increase in magnitude in the coldest months.