As the highest plateau in the world, the Tibetan Plateau (TP) strongly affects regional weather and climate as well as global atmospheric circulations. Here six reanalysis products (i.e., MERRA, ...NCEP/NCAR‐1, CFSR, ERA‐40, ERA‐Interim, and GLDAS) are evaluated using in situ measurements at 63 weather stations over the TP from the Chinese Meteorological Administration (CMA) for 1992–2001 and at nine stations from field campaigns (CAMP/Tibet) for 2002–2004. The measurement variables include daily and monthly precipitation and air temperature at all CMA and CAMP/Tibet stations as well as radiation (downward and upward shortwave and longwave), wind speed, humidity, and surface pressure at CAMP stations. Four statistical quantities (correlation coefficient, ratio of standard deviations, standard deviation of differences, and bias) are computed, and a ranking approach is also utilized to quantify the relative performance of reanalyses with respect to each variable and each statistical quantity. Compared with measurements at the 63 CMA stations, ERA‐Interim has the best overall performance in both daily and monthly air temperatures, while MERRA has a high correlation with observations. GLDAS has the best overall performance in both daily and monthly precipitation because it is primarily based on the merged precipitation product from surface measurements and satellite remote sensing, while ERA‐40 and MERRA have the highest correlation coefficients for daily and monthly precipitation, respectively. Compared with measurements at the nine CAMP stations, CFSR shows the best overall performance, followed by GLDAS, although the best ranking scores are different for different variables. It is also found that NCEP/NCAR‐1 reanalysis shows the worst overall performance compared with both CMA and CAMP data. Since no reanalysis product is superior to others in all variables at both daily and monthly time scales, various reanalysis products should be combined for the study of weather and climate over the TP.
Key Points
Evaluate six reanalysis products with observations over Tibetan Plateau
The performance of each reanalysis is different with different variables
No reanalysis product is superior to others in all variables at all time scales
Since the concept of toughness was introduced to transportation systems, transportation system toughness has received extensive attention from researchers in the field of transportation worldwide. In ...this paper, a methodology for quantifying and assessing the toughness of urban transportation systems is proposed in the context of the New Crown epidemic. Firstly, the definition of urban transportation system toughness in this context is clarified, and the entropy evaluation method is applied to construct the performance curve of urban transportation systems over time. Then, it is proposed to quantify the system's resistance, recovery, and adaptive ability in terms of the change in the cumulative amount of system performance. Finally, the three characteristic abilities of system toughness are organically combined to obtain a comprehensive assessment of system toughness. Example calculations and analyses are carried out in four Chinese cities with different levels of development, and the results show that the performance of urban transportation systems is positively correlated with their levels of development, and all of them fluctuate greatly under the influence of the epidemic, but Wuhan has the strongest resistance and recovery ability of the transportation system, and shows the highest toughness, followed by Lanzhou, Changchun, and Shanghai. The system toughness quantification and assessment methods proposed in this paper provide a reference for research on improving the ability of urban transportation systems to deal with multiple uncertainty disturbances.
A Global Land Cover Climatology Using MODIS Data Broxton, Patrick D.; Zeng, Xubin; Sulla-Menashe, Damien ...
Journal of applied meteorology and climatology,
06/2014, Volume:
53, Issue:
6
Journal Article
Peer reviewed
Open access
Global land cover data are widely used in weather, climate, and hydrometeorological models. The Collection 5.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) product ...is found to have a substantial amount of interannual variability, with 40% of land pixels showing land cover change one or more times during 2001–10. This affects the global distribution of vegetation if any one year or many years of data are used, for example, to parameterize land processes in regional and global models. In this paper, a value-added global 0.5-km land cover climatology (a single representative map for 2001–10) is developed by weighting each land cover type by its corresponding confidence score for each year and using the highest-weighted land cover type in each pixel in the 2001–10 MODIS data. The climatology is validated by comparing it with the System for Terrestrial Ecosystem Parameterization database as well as additional pixels that are identified from the Google Earth proprietary software database. When compared with the data of any individual year, this climatology does not substantially alter the overall global frequencies of most land cover classes but does affect the global distribution of many land cover classes. In addition, it is validated as well as or better than the MODIS data for individual years. Also, it is based on higher-quality data and is validated better than the Global Land Cover Characteristics database, which is based on 1 year of Advanced Very High Resolution Radiometer data and represents a widely used first-generation global product.
Glaciers in the eastern Hindukush, western Karakoram, and northwestern Himalayan mountain ranges of Northern Pakistan are not responding to global warming in the same manner as their counterparts ...elsewhere. Their retreat rates are less than the global average, and some are either stable or growing. Various investigations have questioned the role of climatic factors in regard to this anomalous behavior, widely referred to as “The Karakoram Anomaly.” Here, for the first time, we present a hydrometeorological perspective based on five decades of synoptic weather observations collected by the meteorological network of Pakistan. Analysis of this unique data set indicates that increased regional scale humidity, cloud cover, and precipitation, along with decreased net radiation, near‐surface wind speed, potential evapotranspiration, and river flow, especially during the summer season, represent a substantial change in the energy, mass, and momentum fluxes that are facilitating the establishment of the Karakoram anomaly.
Key Points
The overall hydrologic change in mass for catchments in the Hindukush‐Karakoram‐Himalaya region, centered in Northern Pakistan, is positive
Changes in precipitation, humidity, cloud, river flow, and wind suggest that this region is becoming moisture surplus and energy deficient
Changes in energy, mass, and momentum fluxes are facilitating establishment of the Karakoram anomaly
Plain Language Summary
The “Karakoram Anomaly” is a term that is used to describe the fact that glaciers in the eastern Hindukush, western Karakoram, and northwestern Himalayan mountain ranges of Northern Pakistan are not responding to global warming in the same manner as their counterparts elsewhere. Specifically, their rates of retreat are less than the global average, and some of the glaciers are either stable or even growing. This remarkable phenomenon has therefore become a popular news topic, and even an excuse for some people to question whether global warming is actually occurring. Our analysis of in situ hydroclimatic variables and river inflows indicates that there is a clear scientific explanation for this localized phenomenon. It is true that glacier melt contributions to river flows during the summer season are decreasing, in spite of the fact that the precipitation has been increasing. But the reason that the glaciers in this region are not melting at increased rates is that summer season cloudiness has increased, which blocks the incoming solar radiation and thereby lowers the amount of heat energy available for the melting process. Combined with the facts that humidity has increased and near‐surface wind speeds have decreased, this has also resulted in reduced moisture loss through evapotranspiration. Together, these conditions have resulted in reduced rates of glacier melting in this region. These findings explain and support the fact that “Karakoram Anomaly” is a real, albeit localized, phenomenon.
The National Center for Atmospheric Research (NCAR) Community Land Model Version 3.5 (CLM3.5) has significantly improved the simulation of hydrologic cycles compared to its earlier version (CLM3.0) ...owing to a series of new and modified parameterizations for canopy and soil processes. One of the key elements is the addition of a soil resistance to effectively reduce soil evaporation (Es) and improve the partitioning of evapotranspiration. This soil resistance, however, is found to be physically inconsistent under wet soil conditions and implicitly include the effects of dead leaves. A new treatment with three components are proposed here: (1) two different approaches to better reflect the soil moisture limitation to Es, the so‐called α and β methods combined and a new soil resistance; (2) a new surface resistance to explicitly represent the effect of plant litter cover on water vapor transfer; and (3) an explicit consideration of the effect of under‐canopy atmospheric stability on the under‐canopy turbulent resistance. The effects of each modification vary locally and seasonally, and their combination leads to regional differences between CLM3.5 and our new formulations. Our new formulations tend to have higher Es over high latitudes and similar or slightly higher Es in dry regions. A larger reduction of Es by the new formulations is also found over regions with relatively wet soil and more vegetation, in better agreement with previous ET partitioning studies.
Reanalysis products produced at the various centers around the globe are utilized formany different scientific endeavors, including forcing land surface models and creating surface flux estimates. ...Here, flux tower observations of temperature, wind speed, precipitation, downward shortwave radiation, net surface radiation, and latent and sensible heat fluxes are used to evaluate the performance of various reanalysis products NCEP–NCAR reanalysis and Climate Forecast System Reanalysis (CFSR) from NCEP; 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) from ECMWF; and Modern-Era Retrospective Analysis for Research and Applications (MERRA) and Global Land Data Assimilation System (GLDAS) from the Goddard Space Flight Center (GSFC). To combine the biases and standard deviation of errors from the separate stations, a ranking system is utilized. It is found that ERA-Interim has the lowest overall bias in 6-hourly air temperature, followed closely by MERRA and GLDAS. The variability in 6-hourly air temperature is again most accurate in ERA-Interim. ERA-40 is found to have the lowest overall bias in latent heat flux, followed closely by CFSR, while ERA-40 also has the lowest 6-hourly sensible heat bias. MERRA has the second lowest and is close to ERA-40. The variability in 6-hourly precipitation is best captured by GLDAS and ERA-Interim, and ERA-40 has the lowest precipitation bias. It is also found that at monthly time scales, the bias term in the reanalysis products are the dominant cause of the mean square errors, while at 6-hourly and daily time scales the dominant contributor to the mean square errors is the correlation term. Also, it is found that the hourly CFSR data have discontinuities present due to the assimilation cycle, while the hourly MERRA data do not contain these jumps.
Precipitation, geopotential height, and wind fields from 21 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are examined to determine how well this generation of general ...circulation models represents the North American monsoon system (NAMS). Results show no improvement since CMIP3 in the magnitude (root-mean-square error and bias) of the mean annual cycle of monthly precipitation over a core monsoon domain, but improvement in the phasing of the seasonal cycle in precipitation is notable. Monsoon onset is early for most models but is clearly visible in daily climatological precipitation, whereas monsoon retreat is highly variable and unclear in daily climatological precipitation. Models that best capture large-scale circulation patterns at a low level usually have realistic representations of the NAMS, but even the best models poorly represent monsoon retreat. Difficulty in reproducing monsoon retreat results from an inaccurate representation of gradients in low-level geopotential height across the larger region, which causes an unrealistic flux of low-level moisture from the tropics into the NAMS region that extends well into the postmonsoon season. Composites of the models with the best and worst representations of the NAMS indicate that adequate representation of the monsoon during the early to midseason can be achieved even with a large-scale circulation pattern bias, as long as the bias is spatially consistent over the larger region influencing monsoon development; in other words, as with monsoon retreat, it is the inaccuracy of the spatial gradients in geopotential height across the larger region that prevents some models from realistic representation of the early and midseason monsoon system.
Many of the scientific and societal challenges in understanding and preparing for global environmental change rest upon our ability to understand and predict the water cycle change at large river ...basin, continent, and global scales. However, current large‐scale land models (as a component of Earth System Models, or ESMs) do not yet reflect the best hydrologic process understanding or utilize the large amount of hydrologic observations for model testing. This paper discusses the opportunities and key challenges to improve hydrologic process representations and benchmarking in ESM land models, suggesting that (1) land model development can benefit from recent advances in hydrology, both through incorporating key processes (e.g., groundwater‐surface water interactions) and new approaches to describe multiscale spatial variability and hydrologic connectivity; (2) accelerating model advances requires comprehensive hydrologic benchmarking in order to systematically evaluate competing alternatives, understand model weaknesses, and prioritize model development needs, and (3) stronger collaboration is needed between the hydrology and ESM modeling communities, both through greater engagement of hydrologists in ESM land model development, and through rigorous evaluation of ESM hydrology performance in research watersheds or Critical Zone Observatories. Such coordinated efforts in advancing hydrology in ESMs have the potential to substantially impact energy, carbon, and nutrient cycle prediction capabilities through the fundamental role hydrologic processes play in regulating these cycles.
Key Points:
Land model development can benefit from recent advances in hydrology
Accelerating modeling advances requires comprehensive benchmarking activities
Stronger collaboration is needed between the hydrology and ESM modeling communities
The quality of simulated soil hydrological variables (i.e., soil moisture, evapotranspiration, and runoff) is largely dependent on the accuracy of meteorological forcing data, especially ...precipitation and air temperature. This issue is quantitatively addressed here by running the Community Land Model (CLM3.5) over China from 1993 to 2002 using the reanalysis‐based precipitation and air temperature and in situ observations in the meteorological forcing data set. Compared to the in situ measured soil moisture data, the CLM3.5 simulation can generally capture the spatial and seasonal variations of soil moisture but produces too‐wet soil in northeastern and eastern China and too‐dry soil in northwestern China. This deficiency is significantly reduced when the in situ measured precipitation data are used to drive the model. An index is also constructed to quantify the sensitivities of soil hydrological variables to variations of precipitation and air temperature. The highest sensitivity of surface hydrological variables to precipitation appears over semiarid regions, while the sensitivity to air temperature for different variables varies regionally (semiarid regions for runoff and soil moisture and humid regions for evapotranspiration (ET)). Over semiarid regions, precipitation and air temperature are equally important to the simulations of soil hydrological variables. Over humid regions, in contrast, ET is more dependent on air temperature than on precipitation, while soil moisture and runoff are less affected by air temperature.
Eight Earth System Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are evaluated, focusing on both the net carbon dioxide flux and its components and their relation with ...climatic variables (temperature, precipitation, and soil moisture) in the historical (1850–2005) and representative concentration pathway 4.5 (RCP4.5; 2006–2100) simulations. While model results differ, their median globally averaged production and respiration terms from 1976 to 2005 agree reasonably with available observation based products. Disturbances such as land use change are roughly represented but crucial in determining whether the land is a carbon source or sink overmany regions in both simulations. While carbon fluxes vary with latitude and between the two simulations, the ratio of net to gross primary production, representing the ecosystem carbon use efficiency, is less dependent on latitude and does not differ significantly in the historical and RCP4.5 simulations. The linear trend of increased land carbon fluxes (except net ecosystem production) is accelerated in the twenty-first century. The cumulative net ecosystem production by 2100 is positive (i.e., carbon sink) in all models and the tropical and boreal latitudes become major carbon sinks in most models. The temporal correlations between annual-mean carbon cycle and climate variables vary substantially (including the change of sign) among the eight models in both the historical and twenty-first-century simulations. The ranges of correlations of carbon cycle variables with precipitation and soil moisture are also quite different, reflecting the important impact of the model treatment of the hydrological cycle on the carbon cycle.
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