scholarly journals Evaluation of High-Resolution Crop Model Meteorological Forcing Datasets at Regional Scale: Air Temperature and Precipitation over Major Land Areas of China

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1011
Author(s):  
Qiuling Wang ◽  
Wei Li ◽  
Chan Xiao ◽  
Wanxiu Ai
Keyword(s):  
High Temperature ◽  
High Resolution ◽  
Air Temperature ◽  
Regional Scale ◽  
Hydrological Process ◽  
China Meteorological Administration ◽  
Meteorological Forcing ◽  
Factors Affecting ◽  
Temperature And Precipitation ◽  
Meteorological Modeling

Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development.

scholarly journals Response of seasonal soil freeze depth to climate change across China

2017 ◽  
Vol 11 (3) ◽  
pp. 1059-1073 ◽  
Author(s):  
Xiaoqing Peng ◽  
Tingjun Zhang ◽  
Oliver W. Frauenfeld ◽  
Kang Wang ◽  
Bin Cao ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Snow Depth ◽  
Vegetation Index ◽  
Regional Scale ◽  
Ground Surface ◽  
Driving Factors ◽  
Hydrological Process ◽  
Vegetation Growth ◽  
Air Temperatures

Abstract. The response of seasonal soil freeze depth to climate change has repercussions for the surface energy and water balance, ecosystems, the carbon cycle, and soil nutrient exchange. Despite its importance, the response of soil freeze depth to climate change is largely unknown. This study employs the Stefan solution and observations from 845 meteorological stations to investigate the response of variations in soil freeze depth to climate change across China. Observations include daily air temperatures, daily soil temperatures at various depths, mean monthly gridded air temperatures, and the normalized difference vegetation index. Results show that soil freeze depth decreased significantly at a rate of −0.18 ± 0.03 cm yr−1, resulting in a net decrease of 8.05 ± 1.5 cm over 1967–2012 across China. On the regional scale, soil freeze depth decreases varied between 0.0 and 0.4 cm yr−1 in most parts of China during 1950–2009. By investigating potential climatic and environmental driving factors of soil freeze depth variability, we find that mean annual air temperature and ground surface temperature, air thawing index, ground surface thawing index, and vegetation growth are all negatively associated with soil freeze depth. Changes in snow depth are not correlated with soil freeze depth. Air and ground surface freezing indices are positively correlated with soil freeze depth. Comparing these potential driving factors of soil freeze depth, we find that freezing index and vegetation growth are more strongly correlated with soil freeze depth, while snow depth is not significant. We conclude that air temperature increases are responsible for the decrease in seasonal freeze depth. These results are important for understanding the soil freeze–thaw dynamics and the impacts of soil freeze depth on ecosystem and hydrological process.

scholarly journals Revisiting the Global Seasonal Snow Classification: An Updated Dataset for Earth System Applications

2021 ◽  
Author(s):  
Matthew Sturm ◽  
Glen E. Liston
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Air Temperature ◽  
Classification System ◽  
European Space Agency ◽  
Physical Geography ◽  
Meteorological Model ◽  
Seasonal Snow ◽  
Temperature And Precipitation ◽  
Global Datasets

AbstractTwenty-five years ago, we published a global seasonal snow classification now widely used in snow research, physical geography, and as a mission planning tool for remote sensing snow studies. Performing the classification requires global datasets of air temperature, precipitation, and land-cover. When introduced in 1995, the finest resolution global datasets of these variables were on a 0.5° × 0.5° latitude-longitude grid (approximately 50 km). Here we revisit the snow classification system and, using new datasets and methods, present a revised classification on a 10-arcsecond × 10-arcsecond latitude-longitude grid (approximately 300 m). We downscaled 0.1° × 0.1° latitude-longitude (approximately 10 km) gridded meteorological climatologies (1981-2019, European Centre for Medium-Range Weather Forecasts [ECMWF] ReAnalysis, 5th Generation Land [ERA5-Land]) using MicroMet, a spatially distributed, high-resolution, micro-meteorological model. The resulting air temperature and precipitation datasets were combined with European Space Agency (ESA) Climate Change Initiative (CCI) GlobCover land-cover data (as a surrogate for wind speed) to produce the updated classification, which we have applied to all of Earth’s terrestrial areas. We describe this new, high-resolution snow classification dataset, highlight the improvements added to the classification system since its inception, and discuss the utility of the climatological snow classes at this much higher resolution. The snow class dataset (Global Seasonal-Snow Classification 2.0) and the tools used to develop the data are publicly available online at the National Snow and Ice Data Center (NSIDC).

scholarly journals Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM model

2015 ◽  
Vol 19 (6) ◽  
pp. 2717-2736 ◽  
Author(s):  
A. Kuentz ◽  
T. Mathevet ◽  
J. Gailhard ◽  
B. Hingray
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Air Temperature ◽  
Regional Scale ◽  
Precipitation Time Series ◽  
Temporal Scales ◽  
Spatial Homogeneity ◽  
Temperature And Precipitation ◽  
Spatio Temporal

Abstract. Efforts to improve the understanding of past climatic or hydrologic variability have received a great deal of attention in various fields of geosciences such as glaciology, dendrochronology, sedimentology and hydrology. Based on different proxies, each research community produces different kinds of climatic or hydrologic reanalyses at different spatio-temporal scales and resolutions. When considering climate or hydrology, many studies have been devoted to characterising variability, trends or breaks using observed time series representing different regions or climates of the world. However, in hydrology, these studies have usually been limited to short temporal scales (mainly a few decades and more rarely a century) because they require observed time series (which suffer from a limited spatio-temporal density). This paper introduces ANATEM, a method that combines local observations and large-scale climatic information (such as the 20CR Reanalysis) to build long-term probabilistic air temperature and precipitation time series with a high spatio-temporal resolution (1 day and a few km2). ANATEM was tested on the reconstruction of air temperature and precipitation time series of 22 watersheds situated in the Durance River basin, in the French Alps. Based on a multi-criteria and multi-scale diagnosis, the results show that ANATEM improves the performance of classical statistical models – especially concerning spatial homogeneity – while providing an original representation of uncertainties which are conditioned by atmospheric circulation patterns. The ANATEM model has been also evaluated for the regional scale against independent long-term time series and was able to capture regional low-frequency variability over more than a century (1883–2010).

scholarly journals Response of seasonal soil freeze depth to climate change across China

2016 ◽  
Author(s):  
Xiaoqing Peng ◽  
Oliver W. Frauenfeld ◽  
Tingjun Zhang ◽  
Kang Wang ◽  
Bin Cao ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Snow Depth ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Regional Scale ◽  
Soil Nutrient ◽  
Hydrological Process ◽  
Soil Temperatures

Abstract. Abstract. The response of seasonal soil freeze depth to climate change has repercussions for the surface energy and water balance, ecosystems, the carbon cycle, and soil nutrient exchange. In this study, we use data from 845 meteorological stations to investigate the response of variations in soil freeze depth to climate change across China. Observations include daily air temperature, daily soil temperatures at various depths, mean monthly gridded air temperature, and Normalized Difference Vegetation Index. Results show that soil freeze depth decreased significantly at a rate of −0.18 cm/year, resulting in a net decrease of 8.05 cm over 1967–2012 across China. On the regional scale, soil freeze depth decreases varied between 0.0 and 0.4 cm/year in most parts of China from 1950 to 2009. Combining climatic and non-climatic factors with soil freeze depth, we conclude that air temperature increases are responsible for the decrease in soil seasonal freeze depth during this period. Changes in snow depth and vegetation are negatively correlated with soil freeze depth. These results are important for understanding the soil freeze/thaw dynamics and the impacts of soil freeze depth on ecosystem and hydrological process.

scholarly journals Combined Dynamical and Statistical Downscaling for High-Resolution Projections of Multiple Climate Variables in the Beijing–Tianjin–Hebei Region of China

2019 ◽  
Vol 58 (11) ◽  
pp. 2387-2403 ◽  
Author(s):  
Zhenyu Han ◽  
Ying Shi ◽  
Jia Wu ◽  
Ying Xu ◽  
Botao Zhou
Keyword(s):  
High Resolution ◽  
Statistical Downscaling ◽  
Climate Model ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Extreme Heat ◽  
China Meteorological Administration ◽  
Plain Area ◽  
Temperature And Precipitation ◽  
Projected Changes

AbstractHigh-resolution combined dynamical and statistical downscaling for multivariables (HDM) was performed in the Beijing–Tianjin–Hebei (BTH) region by using observations from China Meteorological Administration Land Data Assimilation System (CLDAS), a regional climate model (RCM), and quantile mapping. This resulted in the production of a daily product with six variables (daily mean, maximum, and minimum temperature; precipitation; relative humidity; and wind speed), five ensemble members, a multidecadal time span (1980–2099), and a high resolution (6.25 km) for climate change projections under the RCP4.5 scenario. The evaluation showed that the HDM output could reproduce well the mean states of all variables and most extreme indices except the consecutive dry and wet days. The biases in the magnitude of interannual variability in HDM were mostly inherited from the RCM. By using the HDM, future projection over BTH was conducted. The results indicated that the annual mean temperature and precipitation as well as extreme heat and heavy precipitation events will increase over most regions. The warming magnitudes over the mountainous and coastal area at the northern BTH and the wetting magnitudes over the Daqinghe River basin (DRB) within BTH will be relatively stronger. The increases in extreme heat events will be much larger in the plain area. More than one-half of regions with the large extreme precipitation increase will be located within DRB. Both the number of models with the same sign of change and the ensemble standard deviation were used to estimate the projection uncertainty. The projected changes and uncertainties over DRB and subregions and Xiong’an city within the basin for each season are also discussed.

scholarly journals High-resolution air temperature mapping in urban areas: A review on different modelling techniques

2017 ◽  
Vol 21 (6 Part A) ◽  
pp. 2267-2286 ◽  
Author(s):  
Hamid Taheri-Shahraiyni ◽  
Sahar Sodoudi
Keyword(s):  
High Resolution ◽  
Air Temperature ◽  
Urban Areas ◽  
Temporal Variations ◽  
Future Research ◽  
Original Form ◽  
Factors Affecting ◽  
Temperature Mapping ◽  
Spatio Temporal ◽  
Major Factors

In this study, the importance of air temperature from different aspects (e. g., human and plant health, ecological and environmental processes, urban planning, and modelling) is presented in detail, and the major factors affecting air temperature in urban areas are introduced. Given the importance of air temperature, and the necessity of developing high-resolution spatio-temporal air-temperature maps, this paper categorizes the existing approaches for air temperature estimation into three categories (interpolation, regression, and simulation approaches) and reviews them. This paper focuses on high-resolution air temperature mapping in urban areas, which is difficult due to strong spatio-temporal variations. Different air temperature mapping approaches have been applied to an urban area (Berlin, Germany) and the results are presented and discussed. This review paper presents the advantages, limitations, and shortcomings of each approach in its original form. In addition, the feasibility of utilizing each approach for air temperature modelling in urban areas was investigated. Studies into the elimination of the limitations and shortcomings of each approach are presented, and the potential of developed techniques to address each limitation is discussed. Based upon previous studies and developments, the interpolation, regression and coupled simulation techniques show potential for spatio-temporal modelling of air temperature in urban areas. However, some of the shortcomings and limitations for development of high-resolution spatio-temporal maps in urban areas have not been properly addressed yet. Hence, some further studies into the elimination of remaining limitations, and improvement of current approaches to high-resolution spatio-temporal mapping of air temperature, are introduced as future research opportunities.

scholarly journals Improved Seasonal Prediction of Temperature and Precipitation over Land in a High-Resolution GFDL Climate Model

2015 ◽  
Vol 28 (5) ◽  
pp. 2044-2062 ◽  
Author(s):  
Liwei Jia ◽  
Xiaosong Yang ◽  
Gabriel A. Vecchi ◽  
Richard G. Gudgel ◽  
Thomas L. Delworth ◽  
...  
Keyword(s):  
High Resolution ◽  
Air Temperature ◽  
Climate Model ◽  
Seasonal Prediction ◽  
Boreal Winter ◽  
Optimization Approach ◽  
Temperature And Precipitation ◽  
Model Predictions ◽  
Resolution Model ◽  
High Resolution Model

Abstract This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climate model developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land.

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