scholarly journals Effects of Forest Changes on Summer Surface Temperature in Changbai Mountain, China

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1551
Author(s):  
Jiaqi Zhang ◽  
Xiangjin Shen ◽  
Yanji Wang ◽  
Ming Jiang ◽  
Xianguo Lu
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Land Surface ◽  
Climate Models ◽  
Regional Climate ◽  
Changbai Mountain ◽  
Vegetation Coverage ◽  
Area Index ◽  
Forest Coverage ◽  
Cooling Effects

The area and vegetation coverage of forests in Changbai Mountain of China have changed significantly during the past decades. Understanding the effects of forests and forest coverage change on regional climate is important for predicting climate change in Changbai Mountain. Based on the satellite-derived land surface temperature (LST), albedo, evapotranspiration, leaf area index, and land-use data, this study analyzed the influences of forests and forest coverage changes on summer LST in Changbai Mountain. Results showed that the area and vegetation coverage of forests increased in Changbai Mountain from 2003 to 2017. Compared with open land, forests could decrease the summer daytime LST (LSTD) and nighttime LST (LSTN) by 1.10 °C and 0.07 °C, respectively. The increase in forest coverage could decrease the summer LSTD and LSTN by 0.66 °C and 0.04 °C, respectively. The forests and increasing forest coverage had cooling effects on summer temperature, mainly by decreasing daytime temperature in Changbai Mountain. The daytime cooling effect is mainly related to the increased latent heat flux caused by increasing evapotranspiration. Our results suggest that the effects of forest coverage change on climate should be considered in climate models for accurately simulating regional climate change in Changbai Mountain of China.

scholarly journals Soil Control on Runoff Response to Climate Change in Regional Climate Model Simulations

2005 ◽  
Vol 18 (17) ◽  
pp. 3536-3551 ◽  
Author(s):  
Bart van den Hurk ◽  
Martin Hirschi ◽  
Christoph Schär ◽  
Geert Lenderink ◽  
Erik van Meijgaard ◽  
...  
Keyword(s):  
Climate Change ◽  
Annual Cycle ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Water Storage ◽  
Climate Simulation ◽  
Discharge Data ◽  
And Storage

Abstract Simulations with seven regional climate models driven by a common control climate simulation of a GCM carried out for Europe in the context of the (European Union) EU-funded Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects (PRUDENCE) project were analyzed with respect to land surface hydrology in the Rhine basin. In particular, the annual cycle of the terrestrial water storage was compared to analyses based on the 40-yr ECMWF Re-Analysis (ERA-40) atmospheric convergence and observed Rhine discharge data. In addition, an analysis was made of the partitioning of convergence anomalies over anomalies in runoff and storage. This analysis revealed that most models underestimate the size of the water storage and consequently overestimated the response of runoff to anomalies in net convergence. The partitioning of these anomalies over runoff and storage was indicative for the response of the simulated runoff to a projected climate change consistent with the greenhouse gas A2 Synthesis Report on Emission Scenarios (SRES). In particular, the annual cycle of runoff is affected largely by the terrestrial storage reservoir. Larger storage capacity leads to smaller changes in both wintertime and summertime monthly mean runoff. The sustained summertime evaporation resulting from larger storage reservoirs may have a noticeable impact on the summertime surface temperature projections.

scholarly journals Spatio-temporal vegetation dynamics and relationship with climate over East Africa

2016 ◽  
Author(s):  
John Musau ◽  
Sopan Patil ◽  
Justin Sheffield ◽  
Michael Marshall
Keyword(s):  
Climate Change ◽  
East Africa ◽  
Land Surface ◽  
Global Climate ◽  
Regional Climate ◽  
Spatial Regression ◽  
Climate Change Impacts ◽  
Southern Ethiopia ◽  
Area Index ◽  
Vegetation Feedback

Abstract. Vegetation plays a key role in the global climate system via modification of the water and energy balance. Its coupling to climate is therefore important particularly in the tropics, where severe climate change impacts are expected. Vegetation growth is mutually controlled by temperature and water availability while it modifies regional climate through latent heat flux and changes in albedo. Consequently, understanding how projected climate change will impact vegetation and the forcing of vegetation on climate for various land cover types in East Africa is vital. This study provides an assessment of the vegetation trends in East Africa using Leaf Area Index (LAI) time series for the period 1982 to 2011, lead/lag correlation analysis between LAI and climate, a statistical estimation of vegetation feedback on climate using lagged covariance ratios as well as spatial regression analysis. Our results show few significant changes in current LAI trends though persistent declining vegetation trends are shown from Southern Ethiopia extending through Central Kenya into Central Tanzania. Precipitation (temperature) exerts widespread positive (negative) forcing on lagging vegetation except in forests. Positive correlations between the lagging Antecedent Precipitation Index (API) and LAI were dominant compared to temperature. Positive vegetation feedback on precipitation dominates across the region while a stronger negative forcing is exerted on Tmin compared to Tmax. Spatial dependence was also shown as a key component in the vegetation-climate interactions in the region. Given the vital role of land surface dynamics on local and regional climate, these results provide a valuable point of reference for evaluating the land-atmosphere coupling in the region.

scholarly journals An inversion model for estimating the negative air ion concentration using MODIS images of the Daxing’anling region

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242554
Author(s):  
Cui Yue ◽  
Zhao Yuxin ◽  
Zhang Nan ◽  
Zhang Dongyou ◽  
Yang Jiangning
Keyword(s):  
Air Quality ◽  
Surface Temperature ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Area Index ◽  
Inversion Model ◽  
Forest Coverage ◽  
The Relationship

The negative air ion (NAI) concentration is an essential indicator of air quality and atmospheric pollution. The NAI concentration can be used to monitor air quality on a regional scale and is commonly determined using field measurements. However, obtaining these measurements is time-consuming. In this paper, the relationship between remotely sensed surface parameters (such as land surface temperature, normalized difference vegetation index (NDVI), and leaf area index) obtained from MODIS data products and the measured NAI concentration using a stepwise regression method was analyzed to estimate the spatial distribution of the NAI concentration and verify the precision. The results indicated that the NAI concentration had a negative correlation with temperature, leaf area index (LAI), and gross primary production while it exhibited a positive correlation with the NDVI. The relationship between land surface temperature and the NAI concentration in the Daxing’anling region is expressed by the regression equation of y = -35.51x1 + 11206.813 (R2 = 0.6123). Additionally, the NAI concentration in northwest regions with high forest coverage was higher than that in southeast regions with low forest coverage, suggesting that forests influence the air quality and reduce the impact of environmental pollution. The proposed inversion model is suitable for evaluating the air quality in Daxing’anling and provides a reference for air quality evaluation in other areas. In the future, we will expand the quantity and distribution range of sampling points, conduct continuous observations of NAI concentrations and environmental parameters in the research areas with different land-use types, and further improve the accuracy of inversion results to analyze the spatiotemporal dynamic changes in NAI concentration and explore the possibility of expanding the application areas of NAI monitoring.

scholarly journals Evaluation of coupled regional climate models in representing the local biophysical effects of afforestation over continental China

2021 ◽  
pp. 1-62
Author(s):  
Jun Ge ◽  
Bo Qiu ◽  
Bowen Chu ◽  
Duzitian Li ◽  
Lingling Jiang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Climate Models ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
Heat Fluxes ◽  
Test Bed ◽  
Area Index ◽  
Induced Changes ◽  
Biophysical Effects

AbstractRegional climate models have been widely used to examine the biophysical effects of afforestation, but their performances in this respect have rarely been evaluated. To fill this knowledge gap, an evaluation method based on the “space for time” strategy is proposed here. Using this method, we validate the performances of three regional models, the Regional Climate Model (RegCM), Weather Research and Forecasting (WRF) model and the WRF model run at a convection-permitting resolution (WRF-CP), in representing the local biophysical effects of afforestation over continental China against satellite observations. The results show that WRF and WRF-CP can not accurately describe afforestation-induced changes in surface biophysical properties, e.g. albedo or leaf area index. Second, all models exhibit poor simulations of afforestation-induced changes in latent and sensible heat fluxes. In particular, the observed increase in the summer latent heat due to afforestation is substantially underestimated by all models. Third, the models are basically reasonable in representing the biophysical impact of afforestation on temperature. The cooling of the daily mean surface temperature and 2-meter temperature in summer are reproduced well. Nevertheless, the mechanism driving the cooling effect may be improperly represented by the models. Moreover, the models perform relatively poorly in representing the response of the daily minimum surface temperature to afforestation. This highlights the necessity of evaluating the representation of the biophysical effects by a model before the model is employed to carry out afforestation experiments. This study serves as a test bed for validating regional model performance in this respect.

scholarly journals New Insights in Regional Climate Change: Coupled Land Albedo Change Estimation in Greenland from 1981 to 2017

2020 ◽  
Vol 12 (5) ◽  
pp. 756
Author(s):  
Fei Peng ◽  
Haoran Zhou ◽  
Gong Chen ◽  
Qi Li ◽  
Yongxing Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Spatiotemporal Variation ◽  
Future Climate Change ◽  
Anthropogenic Factors ◽  
The Past

Land albedo is an essential variable in land surface energy balance and climate change. Within regional land, albedo has been altered in Greenland as ice melts and runoff increases in response to global warming against the period of the pre-industrial revolution. The assessment of spatiotemporal variation in albedo is a prerequisite for accurate prediction of ice sheet loss and future climate change, as well as crucial prior knowledge for improving current climate models. In our study, we employed the satellite data product from the global land surface satellite (GLASS) project to obtain the spatiotemporal variation of albedo from 1981 to 2017 using the non-parameter-based M-K (Mann-Kendall) method. It was found that the albedo generally showed a decreasing trend in the past 37 years (−0.013 ± 0.001 decade−1, p < 0.01); in particular, the albedo showed a significant increasing trend in the middle part of the study area but a decreasing trend in the coastal area. The interannual and seasonal variations of albedo showed strong spatial-temporal heterogeneity. Additionally, based on natural and anthropogenic factors, in order to further reveal the potential effects of spatiotemporal variation of albedo on the regional climate, we coupled climate model data with observed data documented by satellite and adopted a conceptual experiment for detections and attributions analysis. Our results showed that both the greenhouse gas forcing and aerosol forcing induced by anthropogenic activities in the past 37 decades were likely to be the main contributors (46.1%) to the decrease of albedo in Greenland. Here, we indicated that overall, Greenland might exhibit a local warming effect based on our study. Albedo–ice melting feedback is strongly associated with local temperature changes in Greenland. Therefore, this study provides a potential pathway to understanding climate change on a regional scale based on the coupled dataset.

scholarly journals Med-CORDEX Initiative for Mediterranean Climate Studies

2016 ◽  
Vol 97 (7) ◽  
pp. 1187-1208 ◽  
Author(s):  
P. M. Ruti ◽  
S. Somot ◽  
F. Giorgi ◽  
C. Dubois ◽  
E. Flaounas ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Mediterranean Climate ◽  
Climate Models ◽  
Regional Climate ◽  
Ocean Dynamics ◽  
Added Value ◽  
Biogeochemical Processes ◽  
The Mediterranean ◽  
Fully Coupled

Abstract The Mediterranean is expected to be one of the most prominent and vulnerable climate change “hotspots” of the twenty-first century, and the physical mechanisms underlying this finding are still not clear. Furthermore, complex interactions and feedbacks involving ocean–atmosphere–land–biogeochemical processes play a prominent role in modulating the climate and environment of the Mediterranean region on a range of spatial and temporal scales. Therefore, it is critical to provide robust climate change information for use in vulnerability–impact–adaptation assessment studies considering the Mediterranean as a fully coupled environmental system. The Mediterranean Coordinated Regional Downscaling Experiment (Med-CORDEX) initiative aims at coordinating the Mediterranean climate modeling community toward the development of fully coupled regional climate simulations, improving all relevant components of the system from atmosphere and ocean dynamics to land surface, hydrology, and biogeochemical processes. The primary goals of Med-CORDEX are to improve understanding of past climate variability and trends and to provide more accurate and reliable future projections, assessing in a quantitative and robust way the added value of using high-resolution and coupled regional climate models. The coordination activities and the scientific outcomes of Med-CORDEX can produce an important framework to foster the development of regional Earth system models in several key regions worldwide.

scholarly journals Impacts of Green Vegetation Fraction Derivation Methods on Regional Climate Simulations

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 281 ◽  
Author(s):  
Jose Manuel Jiménez-Gutiérrez ◽  
Francisco Valero ◽  
Sonia Jerez ◽  
Juan Pedro Montávez
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Spatial Average ◽  
Area Index ◽  
Near Surface ◽  
Vegetation Fraction ◽  
Selection Of

The representation of vegetation in land surface models (LSM) is crucial for modeling atmospheric processes in regional climate models (RCMs). Vegetation is characterized by the green fractional vegetation cover (FVC) and/or the leaf area index (LAI) that are obtained from nearest difference vegetation index (NDVI) data. Most regional climate models use a constant FVC for each month and grid cell. In this work, three FVC datasets have been constructed using three methods: ZENG, WETZEL and GUTMAN. These datasets have been implemented in a RCM to explore, through sensitivity experiments over the Iberian Peninsula (IP), the effects of the differences among the FVC data-sets on the near surface temperature (T2m). Firstly, we noted that the selection of the NDVI database is of crucial importance, because there are important bias in mean and variability among them. The comparison between the three methods extracted from the same NDVI database, the global inventory modeling and mapping studies (GIMMS), reveals important differences reaching up to 12% in spatial average and and 35% locally. Such differences depend on the FVC magnitude and type of biome. The methods that use the frequency distribution of NDVI (ZENG and GUTMAN) are more similar, and the differences mainly depends on the land type. The comparison of the RCM experiments exhibits a not negligible effect of the FVC uncertainty on the monthly T2m values. Differences of 30% in FVC can produce bias of 1 ∘ C in monthly T2m, although they depend on the time of the year. Therefore, the selection of a certain FVC dataset will introduce bias in T2m and will affect the annual cycle. On the other hand, fixing a FVC database, the use of synchronized FVC instead of climatological values produces differences up to 1 ∘ C, that will modify the T2m interannual variability.

scholarly journals weather@home 2: validation of an improved global–regional climate modelling system

2017 ◽  
Vol 10 (5) ◽  
pp. 1849-1872 ◽  
Author(s):  
Benoit P. Guillod ◽  
Richard G. Jones ◽  
Andy Bowery ◽  
Karsten Haustein ◽  
Neil R. Massey ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Extreme Weather Events ◽  
Climate Modelling ◽  
Modelling System

Abstract. Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes in such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows one to generate a very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of the weather@home system (weather@home 2) with a higher-resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid-scale land-surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM temperature biases are overall reduced, in particular the warm bias over eastern Europe, but large biases remain. Precipitation is improved over the Alps in summer, with mixed changes in other regions and seasons. The model is shown to represent the main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales.

scholarly journals Satellite-Observed Surface Temperature Changes after the 2004 Taylor Complex Fire in Alaska

2011 ◽  
Vol 15 (11) ◽  
pp. 1-14 ◽  
Author(s):  
Jianjun Ge
Keyword(s):  
Surface Temperature ◽  
Boreal Forest ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Primary Data ◽  
Burned Area ◽  
Area Index ◽  
Surface Temperature Change ◽  
Temperature Changes

Abstract Land-use and land-cover change has been recognized as a key component in global climate change. In the boreal forest ecosystem, fires often cause significant changes in vegetation structure and surface biophysical characteristics, which in turn dramatically change energy and water balances of land surface. Several studies have characterized fire-induced changes in surface energy balance in boreal ecosystem based on site observations. This study provides satellite-observed impacts of a large fire on surface climate in Alaska’s boreal forest. A land surface temperature (LST) product from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) is used as the primary data. Five years after fire, surface temperature over the burned area increased by an average of 2.0°C in the May–August period. The increase reached a maximum of 3.2°C in the year immediately following the fire. The warm anomaly decreased slightly after the second year but remained until the fifth year of the study. These changes in surface temperature may directly affect surface net radiation and thus partition of surface available energy. By documenting continuous and synoptic surface temperature changes over multiple years, this paper demonstrates the value of Earth Observing System (EOS) observations for land–climate interaction research. The observed characteristics of surface temperature change as well as changes in key surface biophysical parameters such as albedo and leaf area index (LAI) can be used in the next generation of climate models to improve the representation of large-scale ecosystem disturbances.

scholarly journals weather@home 2: validation of an improved global-regional climate modelling system

2016 ◽  
Author(s):  
Benoit P. Guillod ◽  
Andy Bowery ◽  
Karsten Haustein ◽  
Richard G. Jones ◽  
Neil R. Massey ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Extreme Weather Events ◽  
Climate Modelling ◽  
Modelling System

Abstract. Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes of such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows to generate very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of weather@home system (weather@home 2) with a higher resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid scale land surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 km to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM biases are overall reduced, in particular the warm and dry bias over eastern Europe, but large biases remain. The model is shown to represent main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales.

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