AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surfa...AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.展开更多
Critical zone(CZ)plays a vital role in sustaining biodiversity and humanity.However,flux quantification within CZ,particularly in terms of subsurface hydrological partitioning,remains a significant challenge.This stud...Critical zone(CZ)plays a vital role in sustaining biodiversity and humanity.However,flux quantification within CZ,particularly in terms of subsurface hydrological partitioning,remains a significant challenge.This study focused on quantifying subsurface hydrological partitioning,specifically in an alpine mountainous area,and highlighted the important role of lateral flow during this process.Precipitation was usually classified as two parts into the soil:increased soil water content(SWC)and lateral flow out of the soil pit.It was found that 65%–88%precipitation contributed to lateral flow.The second common partitioning class showed an increase in SWC caused by both precipitation and lateral flow into the soil pit.In this case,lateral flow contributed to the SWC increase ranging from 43%to 74%,which was notably larger than the SWC increase caused by precipitation.On alpine meadows,lateral flow from the soil pit occurred when the shallow soil was wetter than the field capacity.This result highlighted the need for three-dimensional simulation between soil layers in Earth system models(ESMs).During evapotranspiration process,significant differences were observed in the classification of subsurface hydrological partitioning among different vegetation types.Due to tangled and aggregated fine roots in the surface soil on alpine meadows,the majority of subsurface responses involved lateral flow,which provided 98%–100%of evapotranspiration(ET).On grassland,there was a high probability(0.87),which ET was entirely provided by lateral flow.The main reason for underestimating transpiration through soil water dynamics in previous research was the neglect of lateral root water uptake.Furthermore,there was a probability of 0.12,which ET was entirely provided by SWC decrease on grassland.In this case,there was a high probability(0.98)that soil water responses only occurred at layer 2(10–20 cm),because grass roots mainly distributed in this soil layer,and grasses often used their deep roots for water uptake during ET.To improve the estimation of soil water dynamics and ET,we established a random forest(RF)model to simulate lateral flow and then corrected the community land model(CLM).RF model demonstrated good performance and led to significant improvements in CLM simulation.These findings enhance our understanding of subsurface hydrological partitioning and emphasize the importance of considering lateral flow in ESMs and hydrological research.展开更多
利用中国气象局国家气象信息中心研发的中国气象局陆面数据同化系统(China Meteorological Administration Land Data Assimilation System,CLDAS)大气近地面强迫资料,驱动美国国家大气研究中心公用陆面模式(Community Land Model,CLM3....利用中国气象局国家气象信息中心研发的中国气象局陆面数据同化系统(China Meteorological Administration Land Data Assimilation System,CLDAS)大气近地面强迫资料,驱动美国国家大气研究中心公用陆面模式(Community Land Model,CLM3.5),对中国新疆地区土壤温度时空分布进行逐小时Off-line模拟(模拟时段为2009—2012年);利用国家土壤温度自动站(新疆区域105站点)数据验证CLDAS驱动场强迫下的CLM3.5模式在中国新疆地区3个土壤层(5cm、20cm和80cm)的土壤温度模拟能力。研究发现:在月变化方面,第1层(5cm)土壤温度模拟与实测值差异最大,在每年7月最大差异达5k左右;第2层(20cm)在每年7月达最大差异(3k左右),而第3层(80cm)在每年7月均模拟的很好。造成这种现象的原因可能因为新疆地区7月前后浅层土壤温度变化剧烈,温度白天最高可达300K以上,昼夜温差大,导致模式不能很好抓住浅层土壤温度的变化趋势。研究还发现,在80cm土壤深度,模式在1月、12月的模拟结果均较前两层差。在日变化方面,研究发现:较浅的两层(5cm和20cm)土壤温度模拟值在夏季和秋季均较差。与月变化模拟结果类似的是,80cm土壤层日变化在1、12月模拟较差,然而在其他时段却模拟的很好。在小时变化方面,分析发现:第1层土壤(5cm)模拟结果在每年的1—4月及9—11月的全天(即24 h),模式也会有不同的偏差:其中,在03UTC—21UTC之间主要表现为模式结果比观测结果偏高,而在日内21UTC—00UTC主要表现为模拟结果偏小。在每年的5—8月,全天模拟值都偏小,其中在09UTC达当日最大值。而距离第2层(20cm)处的土壤温度模拟值在大部分月份都偏差较小(-1K至1k之间),并在日内12UTC偏差达到当日最大值。研究发现,在土壤20cm处,模式模拟的最大值较观测值提前,而第3层(80cm)的土壤温度基本不受日内变化影响,表现较为平稳。造成这种影响的原因可能是因为新疆地区5—8月、9—11月为昼夜温差大,深层土壤温度较浅层土壤温度温差变化小,这也造成了模式对于浅层土壤模拟较深层差的主要原因。总体研究表明:CLDAS驱动场强迫下的CLM3.5模式可较为精确的模拟中国新疆地区多年平均土壤温度时空分布,并较为准确的反映中国新疆地区土壤温度的小时、日、月及年际的变化规律。模式浅温度模拟不好的原因可能与模式参数化方案及地表参数有关,后期将继续修正该问题。展开更多
利用第二次全国土壤调查土壤质地数据（SNSS）和中国区域陆地覆盖资料（CLCV）将陆面过程模式CLM3.5（Community Land Model version 3.5）中基于联合国粮食农业组织发展的土壤质地数据（FAO）和MODIS卫星反演的陆地覆盖数据（MODIS）...利用第二次全国土壤调查土壤质地数据（SNSS）和中国区域陆地覆盖资料（CLCV）将陆面过程模式CLM3.5（Community Land Model version 3.5）中基于联合国粮食农业组织发展的土壤质地数据（FAO）和MODIS卫星反演的陆地覆盖数据（MODIS）进行了替换,使用中国气象局陆面数据同化系统（CMA Land Data Assimilation System,CLDAS）大气强迫场资料,分别驱动基于同时改进土壤质地和陆地覆盖数据的CLM3.5（CLM-new）、基于只改进陆地覆盖数据的CLM3.5（CLM-clcv）、基于只改进土壤质地数据的CLM3.5（CLM-snss）和基于原始下垫面数据的CLM3.5（CLM-ctl）,对内蒙古地区2011~2013年土壤湿度的时空变化进行模拟试验,研究下垫面改进对CLM3.5模拟土壤湿度的影响。将四组模拟结果与46个土壤水分站点观测数据进行对比分析,结果表明：相对于控制试验,CLM-clcv、CLM-snss和CLM-new都能不同程度地改进土壤湿度模拟,其中CLM-clcv主要在呼伦贝尔改进明显,CLM-snss则在除呼伦贝尔以外的大部地区改进显著,CLM-ctl模拟的土壤湿度在各层上均系统性偏大,而CLM-new模拟土壤湿度最好地反映出内蒙古地区观测的土壤湿度的时空变化特征,显著改善了土壤湿度的模拟,体现在与观测值有着更高的相关系数和更小的平均偏差与均方根误差。展开更多
陆面模式CLM(Community Land Model)是目前国际上发展较为完善并被广泛应用的陆面过程模式。本文使用中国科学院寒区旱区环境与工程研究所位于青藏高原东部的若尔盖高原湿地生态系统研究站的观测资料,对CLM3.0版本及CLM4.0版本在上述地...陆面模式CLM(Community Land Model)是目前国际上发展较为完善并被广泛应用的陆面过程模式。本文使用中国科学院寒区旱区环境与工程研究所位于青藏高原东部的若尔盖高原湿地生态系统研究站的观测资料,对CLM3.0版本及CLM4.0版本在上述地区的模拟性能进行了检验与对比。通过比较观测值与模拟值,验证了模式在高原季节性冻土地区的适用性,发现CLM4.0较CLM3.0在模拟结果上有了一定提高。CLM4.0加入了未冻水参数化方案,使模式可以模拟到冬季土壤冻结后存留的未冻水,显著增加了冻融期间土壤含水量的模拟,同时减小了土壤含冰量的模拟值。并因此增大了模拟的冻土热容量,减小了热导率,使冻融期间土壤温度的模拟也有了一定改善。但是模拟中也发现对于较深层土壤,温度模拟值在冻融期间较观测显著偏低。另外,在消融(冻结)过程阶段CLM4.0模拟的土壤含水量骤增(骤降)的时间均较观测提前。消融过程、冻结过程阶段模拟时间偏短,而完全冻结、完全消融阶段模拟时间偏长。因此CLM对于高原冻土地区的模拟仍是其需要重点改进的地方之一。展开更多
利用NOAH(The Community Noah Land Surface Model)、SHAW(Simultaneous Heat and Water)和CLM(Community Land Model)3个不同的陆面过程模式及兰州大学(Semi-Arid Climate Observatory and Laboratory,SACOL)2007年的观测资料,对黄土...利用NOAH(The Community Noah Land Surface Model)、SHAW(Simultaneous Heat and Water)和CLM(Community Land Model)3个不同的陆面过程模式及兰州大学(Semi-Arid Climate Observatory and Laboratory,SACOL)2007年的观测资料,对黄土高原半干旱区的陆面过程进行了模拟研究。通过与观测值间的对比,考察不同陆面过程模式在半干旱区的适用性。研究结果表明:3个模式在半干旱区的模拟性能有较大差异。其中,CLM模式模拟的20 cm以上的浅层土壤温度最优,SHAW模式模拟的深层土壤温度最优;SHAW模式模拟的土壤含水量与观测值最为接近,而NOAH和CLM模式模拟值有较大偏差;3个模式均能较好地模拟地表反射辐射,其中SHAW模式模拟值与观测值的偏差最小;对地表长波辐射的模拟,CLM模式的模拟最优;3个模式均能较好地反映感热、潜热通量的变化趋势,其中CLM模式对感热的模拟性能优于其他两个模式,在有降水发生后的湿润条件下,CLM模式对潜热的模拟性能最优,而无降水的干燥条件下,CLM模式的模拟偏差最大,NOAH模式对冬季潜热的模拟最优。总体而言,CLM模式能够更好地再现半干旱区地气之间的相互作用,但模式对土壤含水量及干燥条件下的潜热通量的模拟较差,模式对半干旱区陆气间的水文过程还有待进一步的研究和改进。展开更多
利用CLM(Common Land Model)模式对我国内蒙古奈曼旗农牧交错带沙漠和农田两种不同典型下垫面的陆面过程进行了数值模拟试验,并与外场试验观测结果进行了对比分析。结果表明:无论是沙漠还是农田试验,CLM都能够较好地模拟其辐射通量和土...利用CLM(Common Land Model)模式对我国内蒙古奈曼旗农牧交错带沙漠和农田两种不同典型下垫面的陆面过程进行了数值模拟试验,并与外场试验观测结果进行了对比分析。结果表明:无论是沙漠还是农田试验,CLM都能够较好地模拟其辐射通量和土壤中的热传导特征,CLM的模拟结果能够真实地再现试验期间土壤热传导过程对天气过程的响应。相比而言,模式对沙漠地区长波辐射通量和干燥时期短波辐射通量的模拟结果好于农田,其原因可能是因为农田下垫面植被及土壤特征较沙漠复杂,有着很大的不确定性,造成了农田地表反照率和温度模拟的偏差。而对农田热传导的模拟结果好于沙漠,反映了CLM对含水量较大、持水力较强的农田下垫面的热传导模拟能力较好,而对含水量较小、持水力较弱的沙漠下垫面的热传导模拟能力相对较差。展开更多
本文基于中国1:100万植被图、马里兰大学AVHRR森林覆盖资料和中国753个气象站点40年的降水气温资料,发展了一套用于气候模拟的中国陆面覆盖资料(Chinese land cover derived fromvegetation map,简称CLCV)。该套资料与CLM(Community Lan...本文基于中国1:100万植被图、马里兰大学AVHRR森林覆盖资料和中国753个气象站点40年的降水气温资料,发展了一套用于气候模拟的中国陆面覆盖资料(Chinese land cover derived fromvegetation map,简称CLCV)。该套资料与CLM(Community Land Model)原来所用的MODIS(Moderate Resolution I maging Spectro-radiometer)陆面覆盖资料相比有较大不同:其中裸土比例减少了14.5%,森林、灌木、草原和农作物比例分别增加了3.3%、4.8%、4.4%和0.3%,冰川、湖泊和湿地比例分别增加了0.4%、0.8%和0.6%。将CLCV和MO-DIS资料分别与全国土地资源概查汇总结果分省统计资料和基于中国1km土地利用图的土地利用资料比较表明,CLCV与两者较为接近。最后,利用CLM模式分别采用CLCV与MODIS陆面覆盖资料在中国区域内进行数值模拟,结果显示,使用CLCV资料所模拟的蒸散增加了约7.7mm/a;地表反照率、感热和径流分别减小了约0.7%、0.3W/m2和7.6mm/a;与MODIS卫星反演地表反照率和GRDC(Global Runoff Data Centre)径流资料比较表明,利用CLCV资料所模拟的地表反照率有一定改进,并能基本模拟出径流分布趋势。展开更多
基金Supported by National Natural Science Foundation of China(No.82160195,No.82460203)Degree and Postgraduate Education Teaching Reform Project of Jiangxi Province(No.JXYJG-2020-026).
文摘AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.
基金funded by the National Natural Science Foundation of China(42371022,42030501,41877148).
文摘Critical zone(CZ)plays a vital role in sustaining biodiversity and humanity.However,flux quantification within CZ,particularly in terms of subsurface hydrological partitioning,remains a significant challenge.This study focused on quantifying subsurface hydrological partitioning,specifically in an alpine mountainous area,and highlighted the important role of lateral flow during this process.Precipitation was usually classified as two parts into the soil:increased soil water content(SWC)and lateral flow out of the soil pit.It was found that 65%–88%precipitation contributed to lateral flow.The second common partitioning class showed an increase in SWC caused by both precipitation and lateral flow into the soil pit.In this case,lateral flow contributed to the SWC increase ranging from 43%to 74%,which was notably larger than the SWC increase caused by precipitation.On alpine meadows,lateral flow from the soil pit occurred when the shallow soil was wetter than the field capacity.This result highlighted the need for three-dimensional simulation between soil layers in Earth system models(ESMs).During evapotranspiration process,significant differences were observed in the classification of subsurface hydrological partitioning among different vegetation types.Due to tangled and aggregated fine roots in the surface soil on alpine meadows,the majority of subsurface responses involved lateral flow,which provided 98%–100%of evapotranspiration(ET).On grassland,there was a high probability(0.87),which ET was entirely provided by lateral flow.The main reason for underestimating transpiration through soil water dynamics in previous research was the neglect of lateral root water uptake.Furthermore,there was a probability of 0.12,which ET was entirely provided by SWC decrease on grassland.In this case,there was a high probability(0.98)that soil water responses only occurred at layer 2(10–20 cm),because grass roots mainly distributed in this soil layer,and grasses often used their deep roots for water uptake during ET.To improve the estimation of soil water dynamics and ET,we established a random forest(RF)model to simulate lateral flow and then corrected the community land model(CLM).RF model demonstrated good performance and led to significant improvements in CLM simulation.These findings enhance our understanding of subsurface hydrological partitioning and emphasize the importance of considering lateral flow in ESMs and hydrological research.
文摘利用中国气象局国家气象信息中心研发的中国气象局陆面数据同化系统(China Meteorological Administration Land Data Assimilation System,CLDAS)大气近地面强迫资料,驱动美国国家大气研究中心公用陆面模式(Community Land Model,CLM3.5),对中国新疆地区土壤温度时空分布进行逐小时Off-line模拟(模拟时段为2009—2012年);利用国家土壤温度自动站(新疆区域105站点)数据验证CLDAS驱动场强迫下的CLM3.5模式在中国新疆地区3个土壤层(5cm、20cm和80cm)的土壤温度模拟能力。研究发现:在月变化方面,第1层(5cm)土壤温度模拟与实测值差异最大,在每年7月最大差异达5k左右;第2层(20cm)在每年7月达最大差异(3k左右),而第3层(80cm)在每年7月均模拟的很好。造成这种现象的原因可能因为新疆地区7月前后浅层土壤温度变化剧烈,温度白天最高可达300K以上,昼夜温差大,导致模式不能很好抓住浅层土壤温度的变化趋势。研究还发现,在80cm土壤深度,模式在1月、12月的模拟结果均较前两层差。在日变化方面,研究发现:较浅的两层(5cm和20cm)土壤温度模拟值在夏季和秋季均较差。与月变化模拟结果类似的是,80cm土壤层日变化在1、12月模拟较差,然而在其他时段却模拟的很好。在小时变化方面,分析发现:第1层土壤(5cm)模拟结果在每年的1—4月及9—11月的全天(即24 h),模式也会有不同的偏差:其中,在03UTC—21UTC之间主要表现为模式结果比观测结果偏高,而在日内21UTC—00UTC主要表现为模拟结果偏小。在每年的5—8月,全天模拟值都偏小,其中在09UTC达当日最大值。而距离第2层(20cm)处的土壤温度模拟值在大部分月份都偏差较小(-1K至1k之间),并在日内12UTC偏差达到当日最大值。研究发现,在土壤20cm处,模式模拟的最大值较观测值提前,而第3层(80cm)的土壤温度基本不受日内变化影响,表现较为平稳。造成这种影响的原因可能是因为新疆地区5—8月、9—11月为昼夜温差大,深层土壤温度较浅层土壤温度温差变化小,这也造成了模式对于浅层土壤模拟较深层差的主要原因。总体研究表明:CLDAS驱动场强迫下的CLM3.5模式可较为精确的模拟中国新疆地区多年平均土壤温度时空分布,并较为准确的反映中国新疆地区土壤温度的小时、日、月及年际的变化规律。模式浅温度模拟不好的原因可能与模式参数化方案及地表参数有关,后期将继续修正该问题。
文摘利用第二次全国土壤调查土壤质地数据（SNSS）和中国区域陆地覆盖资料（CLCV）将陆面过程模式CLM3.5（Community Land Model version 3.5）中基于联合国粮食农业组织发展的土壤质地数据（FAO）和MODIS卫星反演的陆地覆盖数据（MODIS）进行了替换,使用中国气象局陆面数据同化系统（CMA Land Data Assimilation System,CLDAS）大气强迫场资料,分别驱动基于同时改进土壤质地和陆地覆盖数据的CLM3.5（CLM-new）、基于只改进陆地覆盖数据的CLM3.5（CLM-clcv）、基于只改进土壤质地数据的CLM3.5（CLM-snss）和基于原始下垫面数据的CLM3.5（CLM-ctl）,对内蒙古地区2011~2013年土壤湿度的时空变化进行模拟试验,研究下垫面改进对CLM3.5模拟土壤湿度的影响。将四组模拟结果与46个土壤水分站点观测数据进行对比分析,结果表明：相对于控制试验,CLM-clcv、CLM-snss和CLM-new都能不同程度地改进土壤湿度模拟,其中CLM-clcv主要在呼伦贝尔改进明显,CLM-snss则在除呼伦贝尔以外的大部地区改进显著,CLM-ctl模拟的土壤湿度在各层上均系统性偏大,而CLM-new模拟土壤湿度最好地反映出内蒙古地区观测的土壤湿度的时空变化特征,显著改善了土壤湿度的模拟,体现在与观测值有着更高的相关系数和更小的平均偏差与均方根误差。
文摘陆面模式CLM(Community Land Model)是目前国际上发展较为完善并被广泛应用的陆面过程模式。本文使用中国科学院寒区旱区环境与工程研究所位于青藏高原东部的若尔盖高原湿地生态系统研究站的观测资料,对CLM3.0版本及CLM4.0版本在上述地区的模拟性能进行了检验与对比。通过比较观测值与模拟值,验证了模式在高原季节性冻土地区的适用性,发现CLM4.0较CLM3.0在模拟结果上有了一定提高。CLM4.0加入了未冻水参数化方案,使模式可以模拟到冬季土壤冻结后存留的未冻水,显著增加了冻融期间土壤含水量的模拟,同时减小了土壤含冰量的模拟值。并因此增大了模拟的冻土热容量,减小了热导率,使冻融期间土壤温度的模拟也有了一定改善。但是模拟中也发现对于较深层土壤,温度模拟值在冻融期间较观测显著偏低。另外,在消融(冻结)过程阶段CLM4.0模拟的土壤含水量骤增(骤降)的时间均较观测提前。消融过程、冻结过程阶段模拟时间偏短,而完全冻结、完全消融阶段模拟时间偏长。因此CLM对于高原冻土地区的模拟仍是其需要重点改进的地方之一。
文摘利用NOAH(The Community Noah Land Surface Model)、SHAW(Simultaneous Heat and Water)和CLM(Community Land Model)3个不同的陆面过程模式及兰州大学(Semi-Arid Climate Observatory and Laboratory,SACOL)2007年的观测资料,对黄土高原半干旱区的陆面过程进行了模拟研究。通过与观测值间的对比,考察不同陆面过程模式在半干旱区的适用性。研究结果表明:3个模式在半干旱区的模拟性能有较大差异。其中,CLM模式模拟的20 cm以上的浅层土壤温度最优,SHAW模式模拟的深层土壤温度最优;SHAW模式模拟的土壤含水量与观测值最为接近,而NOAH和CLM模式模拟值有较大偏差;3个模式均能较好地模拟地表反射辐射,其中SHAW模式模拟值与观测值的偏差最小;对地表长波辐射的模拟,CLM模式的模拟最优;3个模式均能较好地反映感热、潜热通量的变化趋势,其中CLM模式对感热的模拟性能优于其他两个模式,在有降水发生后的湿润条件下,CLM模式对潜热的模拟性能最优,而无降水的干燥条件下,CLM模式的模拟偏差最大,NOAH模式对冬季潜热的模拟最优。总体而言,CLM模式能够更好地再现半干旱区地气之间的相互作用,但模式对土壤含水量及干燥条件下的潜热通量的模拟较差,模式对半干旱区陆气间的水文过程还有待进一步的研究和改进。
文摘利用CLM(Common Land Model)模式对我国内蒙古奈曼旗农牧交错带沙漠和农田两种不同典型下垫面的陆面过程进行了数值模拟试验,并与外场试验观测结果进行了对比分析。结果表明:无论是沙漠还是农田试验,CLM都能够较好地模拟其辐射通量和土壤中的热传导特征,CLM的模拟结果能够真实地再现试验期间土壤热传导过程对天气过程的响应。相比而言,模式对沙漠地区长波辐射通量和干燥时期短波辐射通量的模拟结果好于农田,其原因可能是因为农田下垫面植被及土壤特征较沙漠复杂,有着很大的不确定性,造成了农田地表反照率和温度模拟的偏差。而对农田热传导的模拟结果好于沙漠,反映了CLM对含水量较大、持水力较强的农田下垫面的热传导模拟能力较好,而对含水量较小、持水力较弱的沙漠下垫面的热传导模拟能力相对较差。
文摘本文基于中国1:100万植被图、马里兰大学AVHRR森林覆盖资料和中国753个气象站点40年的降水气温资料,发展了一套用于气候模拟的中国陆面覆盖资料(Chinese land cover derived fromvegetation map,简称CLCV)。该套资料与CLM(Community Land Model)原来所用的MODIS(Moderate Resolution I maging Spectro-radiometer)陆面覆盖资料相比有较大不同:其中裸土比例减少了14.5%,森林、灌木、草原和农作物比例分别增加了3.3%、4.8%、4.4%和0.3%,冰川、湖泊和湿地比例分别增加了0.4%、0.8%和0.6%。将CLCV和MO-DIS资料分别与全国土地资源概查汇总结果分省统计资料和基于中国1km土地利用图的土地利用资料比较表明,CLCV与两者较为接近。最后,利用CLM模式分别采用CLCV与MODIS陆面覆盖资料在中国区域内进行数值模拟,结果显示,使用CLCV资料所模拟的蒸散增加了约7.7mm/a;地表反照率、感热和径流分别减小了约0.7%、0.3W/m2和7.6mm/a;与MODIS卫星反演地表反照率和GRDC(Global Runoff Data Centre)径流资料比较表明,利用CLCV资料所模拟的地表反照率有一定改进,并能基本模拟出径流分布趋势。
