scholarly journals Influence of Land Cover Change on Regional Water Cycles in Eastern Siberia

2013 ◽  
Vol 52 (2) ◽  
pp. 484-497 ◽  
Author(s):  
Ryuhei Yoshida ◽  
Masahiro Sawada ◽  
Takeshi Yamazaki ◽  
Takeshi Ohta ◽  
Tetsuya Hiyama
Keyword(s):  
Energy Flux ◽  
Land Surface ◽  
Water Surface ◽  
Water Cycle ◽  
Parameter Sensitivity ◽  
Shortwave Radiation ◽  
Downward Shortwave Radiation ◽  
Precipitation Response ◽  
Surface Expansion ◽  
Sensitivity Method

AbstractThis study evaluated the effect of recent eastern Siberian land surface changes, such as water surface expansion, on water-energy fluxes and precipitation and focused on land surface parameters using a three-dimensional atmospheric model [the Japan Meteorological Agency Nonhydrostatic model (JMA-NHM)]. Five parameters were set (viz., surface albedo, evaporative efficiency, roughness length, heat capacity, and thermal conductivity), and a response of evaporation and precipitation was evaluated. Increased precipitation corresponded to 75% of the increased evaporation on interparameter average, indicating strong land–atmosphere coupling. Water-energy flux and precipitation responses to water surface expansion were evaluated by two methods: JMA-NHM and the parameter sensitivity method. The latter method used a linear combination of parameter sensitivity on the fluxes and precipitation and parameter changes with land surface change. JMA-NHM demonstrated an increase in evaporation and precipitation and a decrease in downward shortwave radiation with low-level cloud increases. The parameter sensitivity method gave the same order as JMA-NHM in the estimation. This method has minimal calculation cost; thus, water-energy flux and precipitation response with further water surface expansion and decreases in forest area were simulated, producing various land surface data. The enhancement of the precipitation response to evaporation was weak for further water surface expansion in the largely expanded water surface area; however, the ratio increased dramatically for the small water surface expanding area, indicating intense water cycle enhancement at the beginning of water surface expansion. Although grassland formation from forest has minimal impact, if incoming downward shortwave radiation were to increase because of the disappearance of the forest shading effect and the water surface formed by permafrost melting, the water cycle would be enhanced intensely.

scholarly journals Short-term forecasting of regional biospheric CO<sub>2</sub> fluxes in Europe using a light-use-efficiency model (VPRM, MPI-BGC version 1.2)

2020 ◽  
Vol 13 (9) ◽  
pp. 4091-4106
Author(s):  
Jinxuan Chen ◽  
Christoph Gerbig ◽  
Julia Marshall ◽  
Kai Uwe Totsche
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Type ◽  
Weather Prediction ◽  
Shortwave Radiation ◽  
Light Use Efficiency ◽  
Co2 Fluxes ◽  
Downward Shortwave Radiation ◽  
Use Efficiency ◽  
Light Use Efficiency Model

Abstract. Forecasting atmospheric CO2 concentrations on synoptic timescales (∼ days) can benefit the planning of field campaigns by better predicting the location of important gradients. One aspect of this, accurately predicting the day-to-day variation in biospheric fluxes, poses a major challenge. This study aims to investigate the feasibility of using a diagnostic light-use-efficiency model, the Vegetation Photosynthesis Respiration Model (VPRM), to forecast biospheric CO2 fluxes on the timescale of a few days. As input, the VPRM model requires downward shortwave radiation, 2 m temperature, and enhanced vegetation index (EVI) and land surface water index (LSWI), both of which are calculated from MODIS reflectance measurements. Flux forecasts were performed by extrapolating the model input into the future, i.e., using downward shortwave radiation and temperature from a numerical weather prediction (NWP) model, as well as extrapolating the MODIS indices to calculate future biospheric CO2 fluxes with VPRM. A hindcast for biospheric CO2 fluxes in Europe in 2014 has been done and compared to eddy covariance flux measurements to assess the uncertainty from different aspects of the forecasting system. In total the range-normalized mean absolute error (normalized) of the 5 d flux forecast at daily timescales is 7.1 %, while the error for the model itself is 15.9 %. The largest forecast error source comes from the meteorological data, in which error from shortwave radiation contributes slightly more than the error from air temperature. The error contribution from all error sources is similar at each flux observation site and is not significantly dependent on vegetation type.

scholarly journals The Plumbing of Land Surface Models: Benchmarking Model Performance

2015 ◽  
Vol 16 (3) ◽  
pp. 1425-1442 ◽  
Author(s):  
M. J. Best ◽  
G. Abramowitz ◽  
H. R. Johnson ◽  
A. J. Pitman ◽  
G. Balsamo ◽  
...  
Keyword(s):  
Heat Flux ◽  
Land Surface ◽  
Model Performance ◽  
Land Surface Model ◽  
Shortwave Radiation ◽  
Surface Model ◽  
Land Surface Models ◽  
Downward Shortwave Radiation ◽  
Surface Models ◽  
Physically Based

Abstract The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori—before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs’ performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.

scholarly journals Regional climate hindcast simulations within EURO-CORDEX: evaluation of a WRF multi-physics ensemble

2015 ◽  
Vol 8 (3) ◽  
pp. 603-618 ◽  
Author(s):  
E. Katragkou ◽  
M. García-Díez ◽  
R. Vautard ◽  
S. Sobolowski ◽  
P. Zanis ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Water Cycle ◽  
Regional Climate ◽  
Shortwave Radiation ◽  
Cold Bias ◽  
Total Cloud Cover ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Downward Shortwave Radiation ◽  
Downward Radiation

Abstract. In the current work we present six hindcast WRF (Weather Research and Forecasting model) simulations for the EURO-CORDEX (European Coordinated Regional Climate Downscaling Experiment) domain with different configurations in microphysics, convection and radiation for the time period 1990–2008. All regional model simulations are forced by the ERA-Interim reanalysis and have the same spatial resolution (0.44°). These simulations are evaluated for surface temperature, precipitation, short- and longwave downward radiation at the surface and total cloud cover. The analysis of the WRF ensemble indicates systematic temperature and precipitation biases, which are linked to different physical mechanisms in the summer and winter seasons. Overestimation of total cloud cover and underestimation of downward shortwave radiation at the surface, mostly linked to the Grell–Devenyi convection and CAM (Community Atmosphere Model) radiation schemes, intensifies the negative bias in summer temperatures over northern Europe (max −2.5 °C). Conversely, a strong positive bias in downward shortwave radiation in summer over central (40–60%) and southern Europe mitigates the systematic cold bias over these regions, signifying a typical case of error compensation. Maximum winter cold biases are over northeastern Europe (−2.8 °C); this location suggests that land–atmosphere rather than cloud–radiation interactions are to blame. Precipitation is overestimated in summer by all model configurations, especially the higher quantiles which are associated with summertime deep cumulus convection. The largest precipitation biases are produced by the Kain–Fritsch convection scheme over the Mediterranean. Precipitation biases in winter are lower than those for summer in all model configurations (15–30%). The results of this study indicate the importance of evaluating not only the basic climatic parameters of interest for climate change applications (temperature and precipitation), but also other components of the energy and water cycle, in order to identify the sources of systematic biases, possible compensatory or masking mechanisms and suggest pathways for model improvement.

Validation of global land surface satellite (GLASS) downward shortwave radiation product in the rugged surface

2013 ◽  
Vol 10 (5) ◽  
pp. 812-823 ◽  
Author(s):  
Hua-an Jin ◽  
Ai-nong Li ◽  
Jin-hu Bian ◽  
Zheng-jian Zhang ◽  
Cheng-quan Huang ◽  
...  
Keyword(s):  
Land Surface ◽  
Shortwave Radiation ◽  
Downward Shortwave Radiation ◽  
Global Land ◽  
Rugged Surface

scholarly journals Short-term forecasting of regional biospheric CO<sub>2</sub> fluxes in Europe using a light-use-efficiency model

2019 ◽  
Author(s):  
Jinxuan Chen ◽  
Christoph Gerbig ◽  
Julia Marshall ◽  
Kai Uwe Totsche
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Type ◽  
Weather Prediction ◽  
Shortwave Radiation ◽  
Light Use Efficiency ◽  
Co2 Fluxes ◽  
Downward Shortwave Radiation ◽  
Use Efficiency ◽  
Light Use Efficiency Model

Abstract. Forecasting atmospheric CO2 concentrations on synoptic time scales (~ days) can benefit the planning of field campaigns by better predicting the location of important gradients. One aspect of this, accurately predicting the day-to-day variation in biospheric fluxes poses a major challenge. This research aims to investigate the feasibility of using a diagnostic light-use-efficiency model, the Vegetation Photosynthesis Respiration Model (VPRM), to forecast biospheric CO2 fluxes on the time scale of a few days. As input the VPRM model requires downward shortwave radiation, 2 m temperature, and Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI), both of which are calculated from MODIS reflectance measurements. Flux forecasts were performed by extrapolating the model input into the future, i.e. using downward shortwave radiation and temperature from a numerical weather prediction (NWP) model, as well as extrapolating the MODIS indices to calculate future biospheric CO2 fluxes with VPRM. A hindcast for biospheric CO2 fluxes in Europe in 2014 has been done and compared to eddy covariance flux measurements to assess the uncertainty from different aspects of the forecasting system. In total the range-normalized mean absolute error (normalized) of the 5 day flux forecast at daily timescales is 7.1 %, while the error for the model itself is 15.9 %. The largest forecast error source comes from the meteorological data, which fail to accurately predict cloud cover, leading to overestimated shortwave radiation in the model. The error contribution from all error sources is similar at each flux observation site, and is not significantly dependent on vegetation type.

An Operational Approach for Generating the Global Land Surface Downward Shortwave Radiation Product From MODIS Data

2019 ◽  
Vol 57 (7) ◽  
pp. 4636-4650 ◽  
Author(s):  
Xiaotong Zhang ◽  
Xiang Zhao ◽  
Wenhong Li ◽  
Shunlin Liang ◽  
Dongdong Wang ◽  
...  
Keyword(s):  
Land Surface ◽  
Shortwave Radiation ◽  
Operational Approach ◽  
Modis Data ◽  
Downward Shortwave Radiation ◽  
Global Land

scholarly journals The Plumbing of Land Surface Models: Is Poor Performance a Result of Methodology or Data Quality?

2016 ◽  
Vol 17 (6) ◽  
pp. 1705-1723 ◽  
Author(s):  
Ned Haughton ◽  
Gab Abramowitz ◽  
Andy J. Pitman ◽  
Dani Or ◽  
Martin J. Best ◽  
...  
Keyword(s):  
Energy Conservation ◽  
Latent Heat ◽  
Land Surface ◽  
Empirical Models ◽  
Heat Fluxes ◽  
Shortwave Radiation ◽  
Land Surface Models ◽  
Flux Tower ◽  
Downward Shortwave Radiation ◽  
Surface Models

Abstract The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave radiation, surface air temperature, and relative humidity. These results are explored here in greater detail and possible causes are investigated. It is examined whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation, and whether a lack of energy conservation in flux tower data gives the empirical models an unfair advantage in the intercomparison. It is demonstrated that energy conservation in the observational data is not responsible for these results. It is also shown that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. Finally, evidence is presented that suggests that the nature of this partitioning problem is likely shared among all contributing LSMs. While a single candidate explanation for why land surface models perform poorly relative to empirical benchmarks in PLUMBER could not be found, multiple possible explanations are excluded and guidance is provided on where future research should focus.

scholarly journals A Simple Method for Converting 1-km Resolution Daily Clear-Sky LST into Real LST

2020 ◽  
Vol 12 (10) ◽  
pp. 1641
Author(s):  
Yunfei Zhang ◽  
Yunhao Chen ◽  
Jing Li ◽  
Xi Chen
Keyword(s):  
Land Surface ◽  
Missing Values ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Shortwave Radiation ◽  
Simple Method ◽  
The Real ◽  
Independent Variables ◽  
Clear Sky ◽  
Reconstruction Methods

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

scholarly journals Discontinuous Daily Temperatures in the WATCH Forcing Datasets

2015 ◽  
Vol 16 (1) ◽  
pp. 465-472 ◽  
Author(s):  
Henning W. Rust ◽  
Tim Kruschke ◽  
Andreas Dobler ◽  
Madlen Fischer ◽  
Uwe Ulbrich
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Research Unit ◽  
Climatic Research Unit ◽  
Daily Maximum ◽  
Monthly Means ◽  
Tropical Africa ◽  
Average Temperature ◽  
Surface Models ◽  
The Tropics

Abstract The Water and Global Change (WATCH) forcing datasets have been created to support the use of hydrological and land surface models for the assessment of the water cycle within climate change studies. They are based on 40-yr ECMWF Re-Analysis (ERA-40) or ECMWF interim reanalysis (ERA-Interim) with temperatures (among other variables) adjusted such that their monthly means match the monthly temperature dataset from the Climatic Research Unit. To this end, daily minimum, maximum, and mean temperatures within one calendar month have been subjected to a correction involving monthly means of the respective month. As these corrections can be largely different for adjacent months, this procedure potentially leads to implausible differences in daily temperatures across the boundaries of calendar months. We analyze day-to-day temperature fluctuations within and across months and find that across-months differences are significantly larger, mostly in the tropics and frigid zones. Average across-months differences in daily mean temperature are typically between 10% and 40% larger than their corresponding within-months average temperature differences. However, regions with differences up to 200% can be found in tropical Africa. Particularly in regions where snowmelt is a relevant player for hydrology, a few degrees Celsius difference can be decisive for triggering this process. Daily maximum and minimum temperatures are affected in the same regions, but in a less severe way.

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