Dynamic Downscaling of Summer Precipitation Prediction over China in 1998 Using WRF and CCSM4

To study the prediction of the anomalous precipitation and general circulation for the summer（June–July–August） of1998, the Community Climate System Model Version 4.0（CCSM4.0） integrations were used to drive version 3.2 of the Weather Research and Forecasting（WRF3.2） regional climate model to produce hindcasts at 60 km resolution. The results showed that the WRF model produced improved summer precipitation simulations. The systematic errors in the east of the Tibetan Plateau were removed, while in North China and Northeast China the systematic errors still existed. The improvements in summer precipitation interannual increment prediction also had regional characteristics. There was a marked improvement over the south of the Yangtze River basin and South China, but no obvious improvement over North China and Northeast China. Further analysis showed that the improvement was present not only for the seasonal mean precipitation, but also on a sub-seasonal timescale. The two occurrences of the Mei-yu rainfall agreed better with the observations in the WRF model,but were not resolved in CCSM. These improvements resulted from both the higher resolution and better topography of the WRF model.

Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

Seasonal Prediction of Indian Summer Monsoon Using WRF: A Dynamical Downscaling Perspective

Seasonal forecasting of the Indian summer monsoon by dynamically downscaling the CFSv2 output using a high resolution WRF model over the hindcast period of 1982-2008 has been performed in this study. The April start ensemble mean of the CFSv2 has been used to provide the initial and lateral boundary conditions for driving the WRF. The WRF model is integrated from 1st May through 1st October for each monsoon season. The analysis suggests that the WRF exhibits potential skill in improving the rainfall skill as well as the seasonal pattern and minimizes the meteorological errors as compared to the parent CFSv2 model. The rainfall pattern is simulated quite closer to the observation (IMD) in the WRF model over CFSv2 especially over the significant rainfall regions of India such as the Western Ghats and the central India. Probability distributions of the rainfall show that the rainfall is improved with the WRF. However, the WRF simulates copious amounts of rainfall over the eastern coast of India. Surface and upper air meteorological parameters show that the WRF model improves the simulation of the lower level and upper-level winds, MSLP, CAPE and PBL height. The specific humidity profiles show substantial improvement along the vertical column of the atmosphere which can be directly related to the net precipitable water. The CFSv2 underestimates the specific humidity along the vertical which is corrected by the WRF model. Over the Bay of Bengal, the WRF model overestimates the CAPE and specific humidity which may be attributed to the copious amount of rainfall along the eastern coast of India. Residual heating profiles also show that the WRF improves the thermodynamics of the atmosphere over 700 hPa and 400 hPa levels which helps in improving the rainfall simulation. Improvement in the land surface fluxes is also witnessed in the WRF model.

Dynamic downscaling of near-surface air temperature at the basin scale using WRF-a case study in the Heihe River Basin, China

The spatial resolution of general circulation models （GCMs） is too coarse to represent regional climate variations at the regional, basin, and local scales required for many environmental modeling and impact assessments. Weather research and forecasting model （WRF） is a nextgeneration, fully compressible, Euler non-hydrostatic mesoscale forecast model with a runtime hydrostatic option. This model is useful for downscaling weather and climate at the scales from one kilometer to thousands of kilometers, and is useful for deriving meteorological parameters required for hydrological simulation too. The objective of this paper is to validate WRF simulating 5 km/ 1 h air temperatures by daily observed data of China Meteorological Administration （CMA） stations, and by hourly in-situ data of the Watershed Allied Telemetry Experimental Research Project. The daily validation shows that the WRF simulation has good agreement with the observed data; the R2 between the WRF simulation and each station is more than 0.93; the absolute of meanbias error （MBE） for each station is less than 2℃; and the MBEs of Ejina, Mazongshan and Alxa stations are near zero, with R2 is more than 0.98, which can be taken as an unbiased estimation. The hourly validation shows that the WRF simulation can capture the basic trend of observed data, the MBE of each site is approximately 2℃, the R2 of each site is more than 0.80, with the highest at 0.95, and the computed and observed surface air temperature series show a significantly similar trend.

A Quick Report on a Dynamical Downscaling Simulation over China Using the Nested Model

This paper describes a dynamical downscaling simulation over China using the nested model system,which consists of the modified Weather Research and Forecasting Model(WRF)nested with the NCAR Community Atmosphere Model(CAM).Results show that dynamical downscaling is of great value in improving the model simulation of regional climatic characteristics.WRF simulates regional detailed temperature features better than CAM.With the spatial correlation coefficient between the observation and the simulation increasing from 0.54 for CAM to 0.79 for WRF,the improvement in precipitation simulation is more perceptible with WRF.Furthermore,the WRF simulation corrects the spatial bias of the precipitation in the CAM simulation.

Comparison of a Very-fine-resolution GCM with RCM Dynamical Downscaling in Simulating Climate in China

Regional climate simulation can generally be improved by using an RCM nested within a coarser-resolution GCM.However, whether or not it can also be improved by the direct use of a state-of-the-art GCM with very fine resolution, close to that of an RCM, and, if so, which is the better approach, are open questions. These questions are important for understanding and using these two kinds of simulation approaches, but have not yet been investigated. Accordingly, the present reported work compared simulation results over China from a very-fine-resolution GCM（VFRGCM） and from RCM dynamical downscaling. The results showed that：（1） The VFRGCM reproduces the climatologies and trends of both air temperature and precipitation, as well as inter-monthly variations of air temperature in terms of spatial pattern and amount, closer to observations than the coarse-resolution version of the GCM. This is not the case, however, for the inter-monthly variations of precipitation.（2） The VFRGCM captures the climatology, trend, and inter-monthly variation of air temperature, as well as the trend in precipitation, more reasonably than the RCM dynamical downscaling method.（3） The RCM dynamical downscaling method performs better than the VFRGCM in terms of the climatology and inter-monthly variation of precipitation. Overall,the results suggest that VFRGCMs possess great potential with regard to their application in climate simulation in the future,and the RCM dynamical downscaling method is still dominant in terms of regional precipitation simulation.

Can Artificial Climate Trends in Global Reanalysis be Reduced by Dynamical Downscaling:A Case Study of China

In this study, the ability of dynamical downscaling for reduction of artificial climate trends in global reanalysis is tested in China. Dynamical downscaling is performed using a 60-km horizontal resolution Regional Integrated Environmental Model System （RIEMS） forced by the NCEP-Department of Energy （DOE） reanalysis II （NCEP-2）. The results show that this regional climate model （RCM） can not only produce dynamically consis- tent fine scale fields of atmosphere and land surface in the regional domain, but it also has the ability to minimize artificial climate trends existing in the global reanalysis to a certain extent. As compared to the observed 2-meter temperature anomaly averaged across China, our model can simulate the observed inter-annual variation and variability as well as reduce artificial climate trends in the reanalysis by approximately 0.10℃ decade-1 from 1980 to 2007. The RIEMS can effectively reduce artificial trends in global reanalysis for areas in western China, especially for regions with high altitude mountains and deserts, as well as introduce some new spurious changes in other local regions. The model simulations overesti- mated observed winter trends for most areas in eastern China with the exception of the Tibetan Plateau, and it greatly overestimated observed summer trends in the Si- chuan Basin located in southwest China. This implies that the dynamical downscaling of RCM for long-term trends has certain seasonal and regional dependencies due to imperfect physical processes and parameterizations.

A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).

Assessment of Climate Change in Nicaragua: Analysis of Precipitation and Temperature by Dynamical Downscaling over a 30-Year Horizon

The present study has generated and analyzed Climate Change projections in Nicaragua for the period 2010-2040. The obtained results are to be used for evaluating and planning more resilient transport infrastructures in the next decades. This study has focused its efforts to pay attention into the effect of Climate Change on precipitation and temperature from a mean and extreme event perspective. Dynamical Downscaling approach on a 4 km resolution grid has been chosen as the most appropriate methodology for the estimation of the projected climate, being able to account for local-scale factors like complex topography or local land uses properly. We selected MPI-ESM-MR as the global climate model with the best skill scores in terms of precipitation and temperature in Nicaragua. MPI-ESM-MR was coupled to a mesoscale model. We chose WRF mesoescale model as the most appropriate regional model and we optimized their physical and dynamical options in order to minimize the model uncertainty in Nicaragua. For this, model output against the available in-situ measurements from the national meteorological station network and satellite data were compared. Climate change signal was estimated by comparing the different climate statistics calculated from a model run over an historical period, 1980-2009, with a model run over a projected period, 2010-2040. The obtained results from the projected climate show an increase of the mean temperature between 0.6°C and 0.8°C and an increase of the number of days per year with maximum daily temperatures higher than 35°C. Regarding precipitation, annual projected amounts do not change remarkably with respect to the historical period. However, significant changes in the distribution of the precipitation within the wet period (May-October) were observed. Moreover, an increment between 5% and 10% of the number of days without precipitation is expected. Finally, Intensity-Duration-Frequency (IDF) projected curves show an increment of the rainfall intensity and an increment of extreme precipitation event frequency, especially in the Caribbean basin.

Impact of Spectral Nudging on the Downscaling of Tropical Cyclones in Regional Climate Simulations

This study investigated the simulations of three months of seasonal tropical cyclone （TC） activity over the western North Pacific using the Advanced Research WRF Model. In the control experiment （CTL）, the TC frequency was considerably overestimated. Additionally, the tracks of some TCs tended to have larger radii of curvature and were shifted eastward. The large-scale environments of westerly monsoon flows and subtropical Pacific highs were unreasonably simulated. The overestimated frequency of TC formation was attributed to a strengthened westerly wind field in the southern quadrants of the TC center. In comparison with the experiment with the spectral nudging method, the strengthened wind speed was mainly modulated by large-scale flow that was greater than approximately 1000 km in the model domain. The spurious formation and undesirable tracks of TCs in the CTL were considerably improved by reproducing realistic large-scale atmospheric monsoon circulation with substantial adjustment between large-scale flow in the model domain and large-scale boundary forcing modified by the spectral nudging method. The realistic monsoon circulation took a vital role in simulating realistic TCs. It revealed that, in the downscaling from large-scale fields for regional climate simulations, scale interaction between model-generated regional features and forced large-scale fields should be considered, and spectral nudging is a desirable method in the downscaling method.

Near Future (2016-40) Summer Precipitation Changes over China as Projected by a Regional Climate Model (RCM) under the RCP8.5 Emissions Scenario: Comparison between RCM Downscaling and the Driving GCM

Multi-decadal high resolution simulations over the CORDEX East Asia domain were performed with the regional climate model RegCM3 nested within the Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 （FGOALS-g2）. Two sets of simulations were conducted at the resolution of 50 km, one for present day （1980-2005） and another for near-future climate （2015-40） under the Representative Concentration Pathways 8.5 （RCP8.5） scenario. Results show that RegCM3 adds value with respect to FGOALS-g2 in simulating the spatial patterns of summer total and extreme precipitation over China for present day climate. The major deficiency is that RegCM3 underestimates both total and extreme precipi- tation over the Yangtze River valley. The potential changes in total and extreme precipitation over China in summer under the RCP8.5 scenario were analyzed. Both RegCM3 and FGOALS-g2 results show that total and extreme precipitation tend to increase over northeastern China and the Tibetan Plateau, but tend to decrease over southeastern China. In both RegCM3 and FGOALS-g2, the change in extreme precipitation is weaker than that for total precipitation. RegCM3 projects much stronger amplitude of total and extreme precipitation changes and provides more regional-scale features than FGOALS-g2. A large uncertainty is found over the Yangtze River valley, where RegCM3 and FGOALS-g2 project opposite signs in terms of precipitation changes. The projected change of vertically integrated water vapor flux convergence generally follows the changes in total and extreme precipitation in both RegCM3 and FGOALS-g2, while the amplitude of change is stronger in RegCM3. Results suggest that the spatial pattern of projected precipitation changes may be more affected by the changes in water vapor flux convergence, rather than moisture content itself.

Projected heat wave increasing trends over China based on combined dynamical and multiple statistical downscaling methods

Extensive investigations on the projection of heat waves(HWs)were conducted on the basis of coarse-resolution global climate models(GCMs).However,these investigations still fail to characterise the future changes in HWs regionally over China.PRECIS dynamical downscaling with a horizontal resolution of 25 km×25 km was employed on the basis of GCM-HadCM3 to provide reliable projections on HWs over the Chinese mainland,and six statistical downscaling methods were used for bias correction under RCP4.5 and RCP8.5 scenarios.The multi-method ensemble(MME)of the top three dynamical downscaling methods with good performance was used to project future changes.Results showed that PRECIS primarily replicated the detailed spatiotemporal pattern of HWs.However,PRECIS overestimated the HWs in the Northwest and Southeast and expanded the areas of HWs in the Northeast and Southwest.Three statistical downscaling methods(quantile mapping,CDF-t and quantile delta mapping)demonstrated good performance in improving PRECIS simulation for reproducing HWs.By contrast,parametric-based trend-preserving approaches such as scaled distribution mapping and ISI-MIP are outperformed by the three aforementioned methods in downscaling HWs,particularly in the high latitudes of China.Based on MME projections,at the end of the 21st century,the national average of the number of HW days each year,the length of the longest HW event in the year and the extreme maximum temperature in HW will increase by 3 times,1 time and 1.3℃,respectively,under the RCP4.5 scenario,whilst that under the RCP8.5 scenario will increase by 8 times,3 times and 3.7℃,respectively,relative to 1986-2005.The Northwest is regionally projected to suffer long and hot HWs,whilst the South and Southeast will experience frequent consecutive HWs.Thus,HWs projected by the combined dynamical and statistical downscaling method are highly reliable in projecting HWs over China.

A high-resolution Asia-Pacific regional coupled prediction system with dynamically downscaling coupled data assimilation

A regional coupled prediction system for the Asia-Pacific(AP-RCP)(38°E-180°,20°S-60°N) area has been established.The AP-RCP system consists of WRF-ROMS(Weather Research and Forecast,and Regional Ocean Model System) coupled models combined with local observational information through dynamically downscaling coupled data assimilation(CDA).The system generates 18-day forecasts for the atmosphere and ocean environment on a daily quasi-operational schedule at Pilot National Laboratory for Marine Science and Technology(Qingdao)(QNLM),consisting of 2 different-resolution coupled models:27 km WRF coupled with 9 km ROMS,9 km WRF coupled with 3 km ROMS,while a version of 3 km WRF coupled with 3 km ROMS is in a test mode.This study is a first step to evaluate the impact of high-resolution coupled model with dynamically downscaling CDA on the extended-range predictions,focusing on forecasts of typhoon onset,improved precipitation and typhoon intensity forecasts as well as simulation of the Kuroshio current variability associated with mesoscale oceanic activities.The results show that for realizing the extended-range predictability of atmospheric and oceanic environment characterized by statistics of mesoscale activities,a fine resolution coupled model resolving local mesoscale phenomena with balanced and coherent coupled initialization is a necessary first step.The next challenges include improving the planetary boundary physics and the representation of air-sea and air-land interactions to enable the model to resolve kilometer or sub-kilometer processes.

Impacts of Global Warming on Hydrological Cycles in the Asian Monsoon Region

The hydrologic changes and the impact of these changes constitute a fundamental global-warmingrelated concern. Faced with threats to human life and natural ecosystems, such as droughts, floods, and soil erosion, water resource planners must increasingly make future risk assessments. Though hydrological predictions associated with the global climate change are already being performed, mainly through the use of GCMs, coarse spatial resolutions and uncertain physical processes limit the representation of terrestrial water/energy interactions and the variability in such systems as the Asian monsoon. Despite numerous studies, the regional responses of hydrologic changes resulting from climate change remains inconclusive. In this paper, an attempt at dynamical downsealing of future hydrologic projection under global climate change in Asia is addressed. The authors conducted present and future Asian regional climate simulations which were nested in the results of Atmospheric General Circulation Model （AGCM） experiments. The regional climate model could capture the general simulated features of the AGCM. Also, some regional phenomena such as orographic precipitation, which did not appear in the outcome of the AGCM simulation, were successfully produced. Under global warming, the increase of water vapor associated with the warmed air temperature was projected. It was projected to bring more abundant water vapor to the southern portions of India and the Bay of Bengal, and to enhance precipitation especially over the mountainous regions, the western part of India and the southern edge of the Tibetan Plateau. As a result of the changes in the synoptic flow patterns and precipitation under global warming, the increases of annual mean precipitation and surface runoff were projected in many regions of Asia. However, both the positive and negative changes of seasonal surface runoff were projected in some regions which will increase the flood risk and cause a mismatch between water demand and water availability in the agricultural season.

Regional climate dynamical downscaling over the Tibetan Plateau——From quarter-degree to kilometer-scale

The Tibetan Plateau(TP)possesses the largest cryosphere in the world outside of the Arctic and Antarctic,and is the source of nine major rivers in Asia.The surface environment of the TP has undergone significant changes against the background of global warming.It is projected that the continuation of climate change in the future will result in most of the glaciers and frozen soil disappearing by the end of this century,and freshwater resources will be greatly reduced,on which 22%of the world’s population depends.These environmental changes are of great concern to global society given the influences of the TP on the climate at the global scale.However,great uncertainties exist in global climate simulations over the TP,which affects our ability to properly understand the associated water security crisis.Based on atmospheric dynamics and physical processes,dynamical downscaling can characterize surface conditions more accurately than global simulations,and better simulate and predict regional or local weather and climate situations.With advances in supercomputing,the grid spacing of dynamical downscaling simulations has been continuously increasing,marching the technique into the kilometer-scale era.In this paper,the origin and development of dynamical downscaling in the TP region from the quarter-degree to kilometer scale is firstly introduced,including an assessment of the advantages and disadvantages of dynamical downscaling at the kilometer scale over the TP.Then,the main land surface factors affecting the performance of dynamical downscaling over the TP are described,as well as a brief introduction to a land surface model with specific plateau characteristics.Specifically,it has emerged that perfecting the land surface model and improving the performance of land-atmosphere interaction are the most effective ways to advance the performance of dynamic downscaling in this region.Finally,the challenges and some recommended future research directions are discussed and proposed.

A High-Resolution Modeling Strategy to Assess Impacts of Climate Change for Mesoamerica and the Caribbean

Mesoamerica and the Caribbean are low-latitude regions at risk for the effects of climate change. Global climate models provide large-scale assessment of climate drivers, but, at a horizontal resolution of 100 km, cannot resolve the effects of topography and land use as they impact the local temperature and precipitation that are keys to climate impacts. We developed a robust dynamical downscaling strategy that used the WRF regional climate model to downscale at 4 - 12 km resolution GCM results. Model verification demonstrates the need for such resolution of topography in order to properly simulate temperatures. Precipitation is more difficult to evaluate, being highly variable in time and space. Overall, a 36 km resolution is inadequate;12 km appears reasonable, especially in regions of low topography, but the 4 km resolution provides the best match with observations. This represents a tradeoff between model resolution and the computational effort needed to make simulations. A key goal is to provide climate change specialists in each country with the information they need to evaluate possible future climate change impacts.

Adaptive Statistical Spatial Downscaling of Precipitation Supported by High-Resolution Atmospheric Simulation Data for Mountainous Areas of Nepal

In complex terrain regions, it is very challenging to obtain high accuracy and resolution precipitation data that are required in land hydrological studies. In this study, an adaptive precipitation downscaling method is proposed based on the statistical downscaling model MicroMet. A key input parameter in the MicroMet is the precipitation adjustment factor(PAF) that shows the elevation dependence of precipitation. Its value is estimated conventionally based on station observations and suffers sparse stations in high altitudes. This study proposes to estimate the PAF value and its spatial variability with precipitation data from high-resolution atmospheric simulations and tests the idea in Nepal of South Himalayas, where rainfall stations are relatively dense. The result shows that MicroMet performs the best with the PAF value estimated from the simulation data at the scale of approximately 1.5 degrees. Not only the value at this scale is qualitatively consistent with early knowledge obtained from intensive observations, but also the downscaling performance with this value is better than or comparable to that with the PAF estimated from dense station data. Finally, it is shown that the PAF estimation, although critical, cannot replace the importance of increasing input station density for downscaling.

A high-resolution dataset for lower atmospheric process studies over the Tibetan Plateau from 1981 to 2020

ABSTRACT A high-resolution meteorological dataset(≤10 km)over the Tibetan Plateau(TP)is the foundation for investigating and predicting the weather and climate over Asia.The TP Subregional Dynamical Downscaling(TPSDD)dataset is a newly developed high-spatial-temporal resolution gridded dataset for land‒air exchange pro-cesses and lower atmospheric structure studies over the whole TP region,taking the climate characteristics of each TP subregion into consideration.The dataset spans from 1981 to 2020,covering the TP with a temporal resolution of 2 hr and spatial resolution of 10 km.Meteorological elements of the dataset include near-surface land-air exchange parameters,such as downward/upward long-wave/shortwave radiation flux,sensible heat flux,latent heat flux,etc.In addition,the vertical distributions of 3-dimensional wind,temperature,humidity,and pressure from the surface to the lower stratosphere are also included.Independent evaluations were con-ducted to verify the performance of the TPSDD dataset by compar-ing TPSDD/reanalysis with surface and vertical observations through the calculation of statistical parameters.The results demonstrate the accuracy and superiority of this dataset against reanalysis data,which provides great potential for future climate change research.

Deep learning-based multi-source precipitation merging for the Tibetan Plateau

Due to its complex and diverse terrain, precipitation gauges in the Tibetan Plateau(TP) are sparse, making it difficult to obtain reliable precipitation data for environmental studies. Data merging is a method that can integrate precipitation data from multiple sources to generate high-precision precipitation data. However, the more commonly used methods, such as regression and machine learning, do not usually consider the local correlation of precipitation, so that the spatial pattern of precipitation cannot be reproduced, while deep learning methods do incorporate spatial correlation. To explore the ability of using deep learning methods in merging precipitation data for the TP, this study compared three methods: a deep learning method—a convolutional neural network(CNN) algorithm, a machine learning method—an artificial neural network(ANN) algorithm, and a statistical method based on Extended Triple Collocation(ETC) in merging precipitation from multiple sources(gauged, grid,satellite and dynamic downscaling) over the TP, as well as their performance for hydrological simulations. Dynamic downscaling data driven by global reanalysis data centered on the TP were introduced in the merging process to better reflect the spatial variability of precipitation. The results show that:(1) in terms of the meteorological metrics, the merged data perform better than the gauge interpolation data. By using data merging, the error between the raw multi-source and gauged precipitation can be reduced, and the precipitation detection capability can be greatly improved;(2) The merged precipitation data also perform well in the hydrological evaluation. The Xin’anjiang(XAJ) model parameter calibration experiments at the source of the Yangtze River(SYR) and the source of the Yellow River(SHR) were repeated 300 times to remove uncertainty in the model parameter results. The median Kling-Gupta Efficiency Coefficients(KGE) of simulated runoff from the merged data of the ANN, CNN and ETC methods for the SYR and the SHR are 0.859, 0.864, 0.838 and 0.835, 0.835, 0.789, respectively. Except for the ETC merging data at the SHR, the performance of other merged data was improved compared to the simulation results of the gauged precipitation(KGE=0.807 at the SYR, KGE=0.828 at the SHR);and(3) In contrast to the machine learning ANN method and the statistical ETC method, the deep learning method, CNN, consistently showed better performance.

The reversal of surface air temperature anomalies in China between early and late winter 2021/2022:Observations and predictions

During winter of 2021/2022,the temperature in China is characterized by a warm-to-cold transition,and the average temperature anomaly in February 2022 is−1.6℃,the coldest February in 2013-2022.We revealed the circulation regimes and physical mechanisms associated with this reversal event and demonstrated the advantage of a regional model downscaling over the use of the global model alone in predicting.In early winter,the warm anomalies are mainly related to an anomalous anticyclonic system downstream of a PNA-like(Pacific-North America)Rossby-wave train induced by La Niña.In late winter,due to the circulation response to the central Pacific warming and negative tropical Indian Ocean Dipole(TIOD),two‘−+−’Rossby-wave trains from high latitudes and the tropical Indian Ocean jointly lead to an anomalous cyclonic system in China.Meanwhile,an anticyclonic blocking system on the northern side of Baikal brings strong and cold air to China.These two systems together cause a significant drop in surface air temperature anomaly in China during the late winter.The Beijing Climate Center climate system model(BCC_CSM1.1 m)can essentially predict this temperature reversal in China about five months in advance.However,the reversal amplitude is weaker due to warm deviations over the tropical Pacific Ocean and equatorial Indian Ocean.Using dynamic downscaling,a regional Climate-Weather Research and Forecasting(CWRF)model correctly predicts the cold SAT anomalies in late winter 2021/2022.The regional model depicts more realistic circulation patterns in East Asia;the anomalous cyclonic system in Inner Mongolia accompanied by the northerly anomalies contribute to a lower-than-normal SAT over China.This study reveals the cooperative effect of wave trains from high latitudes and the tropics on the subseasonal temperature reversal and demonstrates a possible solution to improve the forecast skill by dynamic downscaling according to precise characterization of local surface information.