scholarly journals Mechanisms of Climate Change in the Semiarid African Sahel: The Local View

2010 ◽  
Vol 23 (3) ◽  
pp. 743-756 ◽  
Author(s):  
Alessandra Giannini
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Southern Oscillation ◽  
Net Energy ◽  
Moist Static Energy ◽  
Near Surface ◽  
Vertical Stability ◽  
First Case ◽  
Static Energy ◽  
The Impact

Abstract Application of the moist static energy framework to analyses of vertical stability and net energy in the Sahel sheds light on the divergence of projections of climate change. Two distinct mechanisms are sketched. In one, anthropogenic warming changes continental climate indirectly: warming of the oceans increases moist static energy at upper levels, affecting vertical stability globally, from the top down, and driving drying over the Sahel, in a way analogous to the impact of El Niño–Southern Oscillation on the global tropical atmosphere. In the other, the increase in anthropogenic greenhouse gases drives a direct continental change: the increase in net terrestrial radiation at the surface increases evaporation, favoring vertical instability and near-surface convergence from the bottom up. In both cases the surface warms, but in the first precipitation and evaporation decrease, while in the second they increase. In the first case, land surface warming is brought about by the remotely forced decrease in precipitation and consequent decrease in evaporation and increase in net solar radiation at the surface. In the second, it is brought about by the increase in net terrestrial radiation at the surface, amplified by the water vapor feedback associated with an increase in near-surface humidity.

scholarly journals Amplified warming induced by large-scale application of water-saving techniques

2022 ◽  
Author(s):  
Jing Fu ◽  
Shaozhong Kang ◽  
Lu Zhang ◽  
Xiaolin Li ◽  
Pierre Gentine ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Large Scale ◽  
Global Climate ◽  
Substantial Reduction ◽  
Water Saving ◽  
Irrigated Agriculture ◽  
Near Surface ◽  
Irrigation Water Use ◽  
The Impact

Abstract Large-scale agricultural activities can exacerbate global climate change. In the past three decades, over 5 Mha of cultivated land have been equipped with Water-Saving Techniques (WST) in Northwest China to cope with water scarcity. However, the effect of WST on local climate and its mechanisms are not yet understood. Here we quantified the local climatic effect by comparing temperature and humidity at controlled and irrigated sites before and after the large-scale implementation of WST. Results show that the substantial reduction in irrigation water use has led to an average increase of 0.3°C in growing-season temperature and reduced relative humidity by 2%. Near-surface air temperature responds nonlinearly to percentage area of WST and a threshold value of 40% is found before any noticeable warming effect over the study area. Moreover, it is found that regions with relatively humid climates respond more significantly to WST. This study reveals the mechanism of WST on near-surface climate and highlights the importance of incorporating this feedback into sustainable water management and land-surface models for assessing the impact of irrigated agriculture on regional climate change.

scholarly journals Impact of groundwater – soil moisture interaction on evolution of evapotranspiration and air temperature under climate change

2021 ◽  
Author(s):  
Pedro Arboleda ◽  
Agnès Ducharne ◽  
Frédérique Cheruy
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Radiative Forcing ◽  
Change Trajectories ◽  
Near Surface ◽  
The Future ◽  
Evapotranspiration Rate ◽  
The Mean ◽  
The Impact

<p>Groundwater (GW) constitutes by far the largest volume of liquid freshwater on Earth. The most active part is soil moisture (SM), which plays a key role on land/atmosphere interactions. But GW is often stored in deep reservoirs below the soil as well, where it presents slow horizontal movements along hillslopes toward the river network. They end up forming baseflow with well-known buffering effects on streamflow variability, but they also contribute to sustain higher SM values, especially in the lowland areas surrounding streams, which are among the most frequent wetlands.  As a result, GW-SM interactions may influence the climate system, in the past but also in the future, with a potential to alleviate anthropogenic warming, at least regionally, owing to enhanced evapotranspiration rate (ET) or higher soil thermal inertia for instance.<br>To assess where, when, and how much GW-SM interaction affects the climate change trajectories, we use coupled land-atmosphere simulations with the IPSL-CM6 climate model, developed by the Institut Pierre Simon Laplace for CMIP6.  We contrast the results of two long-term simulations (1979-2100), which share the same sea surface temperature and radiative forcing, using the SSP5-8.5 scenario (i.e. the most pessimistic) for 2015-2100. The two simulations differ by their configuration of the land surface scheme ORCHIDEE: in the default version, there is no GW-SM interaction, while this interaction is permitted in the second simulation, within a so-called lowland fraction, fed by surface and GW runoff from the rest of the grid-cell. For simplicity, this lowland fraction is set constant over time, but varies across grid-cells based on a recently designed global scale wetland map. <br>Within this framework, we analyse the impact of the GW-SM interaction on climate change trajectories, focusing on the response of evapotranspiration rates and near-surface air temperatures. The GW-SM interaction can modulate the response to climate change by amplifying, attenuating, or even inverting the climate change trend. Based on yearly mean values over land, we find that the GW-SM interaction amplifies the response of evapotranspiration to climate change, as the mean evapotranspiration rate increases 50% faster over 1980 - 2100 in the simulation with GW-SM interaction. In contrast, the mean warming over land is 1% weaker, shifting from 6.4 to 6.3 °C/100 years; thus attenuated, if the GW-SM interaction is accounted for. In both cases, these values hide important differences across climates and seasons, with mitigation or amplification for both variables, indicating the need for regional and seasonal assessment. We will also further explore how GW-SM interaction impacts the future evolution of heatwaves, in terms of duration and frequency. </p>

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

Evaluating the role of soil physical properties on simulated land-atmosphere interactions over South Africa using coupled atmosphere-hydrological modeling

2021 ◽  
Author(s):  
Zhenyu Zhang ◽  
Patrick Laux ◽  
Joël Arnault ◽  
Jianhui Wei ◽  
Jussi Baade ◽  
...  
Keyword(s):  
South Africa ◽  
Physical Properties ◽  
Land Degradation ◽  
Land Surface ◽  
Soil Physical Properties ◽  
Near Surface ◽  
Soil Database ◽  
Global Soil ◽  
The Impact

<p>Land degradation with its direct impact on vegetation, surface soil layers and land surface albedo, has great relevance with the climate system. Assessing the climatic and ecological effects induced by land degradation requires a precise understanding of the interaction between the land surface and atmosphere. In coupled land-atmosphere modeling, the low boundary conditions impact the thermal and hydraulic exchanges at the land surface, therefore regulates the overlying atmosphere by land-atmosphere feedback processes. However, those land-atmosphere interactions are not convincingly represented in coupled land-atmosphere modeling applications. It is partly due to an approximate representation of hydrological processes in land surface modeling. Another source of uncertainties relates to the generalization of soil physical properties in the modeling system. This study focuses on the role of the prescribed physical properties of soil in high-resolution land surface-atmosphere simulations over South Africa. The model used here is the hydrologically-enhanced Weather Research and Forecasting (WRF-Hydro) model. Four commonly used global soil datasets obtained from UN Food and Agriculture Organization (FAO) soil database, Harmonized World Soil Database (HWSD), Global Soil Dataset for Earth System Model (GSDE), and SoilGrids dataset, are incorporated within the WRF-Hydro experiments for investigating the impact of soil information on land-atmosphere interactions. The simulation results of near-surface temperature, skin temperature, and surface energy fluxes are presented and compared to observational-based reference dataset. It is found that simulated soil moisture is largely influenced by soil texture features, which affects its feedback to the atmosphere.</p>

scholarly journals Dome effect of black carbon and its key influencing factors: A one-dimensional modelling study

2017 ◽  
Author(s):  
Zilin Wang ◽  
Xin Huang ◽  
Aijun Ding
Keyword(s):  
Boundary Layer ◽  
Air Pollution ◽  
Black Carbon ◽  
Rural Areas ◽  
Land Surface ◽  
Critical Role ◽  
Aerosol Layer ◽  
Near Surface ◽  
Haze Pollution ◽  
The Impact

Abstract. Black carbon (BC) has been identified to play a critical role in aerosol-planet boundary layer (PBL) interaction and further deterioration of near-surface air pollution in megacities, which has been named as its dome effect. However, the impacts of key factors that influence this effect, such as the vertical distribution and aging processes of BC, and also the underlying land surface, have not been quantitatively explored yet. Here, based on available in-situ measurements of meteorology and atmospheric aerosols together with the meteorology-chemistry online coupled model, WRF-Chem, we conduct a set of parallel simulations to quantify the roles of these factors in influencing the BC's dome effect and surface haze pollution, and discuss the main implications of the results to air pollution mitigation in China. We found that the impact of BC on PBL is very sensitive to the altitude of aerosol layer. The upper level BC, especially those near the capping inversion, is more essential in suppressing the PBL height and weakening the turbulence mixing. The dome effect of BC tends to be significantly intensified as BC aerosol mixed with scattering aerosols during winter haze events, resulting in a decrease of PBL height by more than 25 %. In addition, the dome effect is more substantial (up to 15 %) in rural areas than that in the urban areas with the same BC loading, indicating an unexpected regional impact of such kind of effect to air quality in countryside. This study suggests that China's regional air pollution would greatly benefit from BC emission reductions, especially those from the elevated sources from the chimneys and also the domestic combustions in rural areas, through weakening the aerosol-boundary layer interactions that triggered by BC.

scholarly journals Sensitivity of biogenic volatile organic compounds to land surface parameterizations and vegetation distributions in California

2016 ◽  
Vol 9 (5) ◽  
pp. 1959-1976 ◽  
Author(s):  
Chun Zhao ◽  
Maoyi Huang ◽  
Jerome D. Fast ◽  
Larry K. Berg ◽  
Yun Qian ◽  
...  
Keyword(s):  
Air Quality ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Land Surface ◽  
Aerosol Formation ◽  
Biogenic Volatile Organic Compounds ◽  
Vegetation Map ◽  
Near Surface ◽  
Volatile Organic ◽  
The Impact

Abstract. Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model with chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.

scholarly journals High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

2020 ◽  
Author(s):  
Benjamin Fersch ◽  
Alfonso Senatore ◽  
Bianca Adler ◽  
Joël Arnault ◽  
Matthias Mauder ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Weather Prediction ◽  
Surface Model ◽  
Near Surface ◽  
Ground Heat Flux ◽  
The Impact ◽  
Fully Coupled ◽  
Regional Water

<p>The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases and energy. Nonlinear feedback and scale dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or bound to traditional viewpoints of definite scientific disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local area weather prediction. We examine the ability of the hydrologically enhanced version of the Weather Research and Forecasting Model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assess the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identical calibrated parameter settings for the land surface model (Noah-MP). The simulations are evaluated based on extensive observations at the pre-Alpine Terrestrial Environmental Observatory (TERENO Pre-Alpine) for the Ammer (600 km²) and Rott (55 km²) river catchments in southern Germany, covering a five month period (Jun–Oct 2016).</p><p>The sensitivity of 7 land surface parameters is tested using the <em>Latin-Hypercube One-factor-At-a-Time</em> (LH-OAT) method and 6 sensitive parameters are subsequently optimized for 6 different subcatchments, using the Model-Independent <em>Parameter Estimation and Uncertainty Analysis software</em> (PEST).</p><p>The calibration of the offline WRF-Hydro leads to Nash-Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of classic WRF and fully coupled WRF-Hydro shows only tiny alterations for radiation and precipitation but considerable changes for moisture- and energy fluxes. By comparison with TERENO Pre-Alpine observations, the fully coupled model slightly outperforms the classic WRF with respect to evapotranspiration, sensible and ground heat flux, near surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation whereas soil moisture and precipitation change randomly.</p>

scholarly journals Assessing the Impact of Changes in Land Surface Conditions on WRF Predictions in Arid Regions

2020 ◽  
Vol 21 (12) ◽  
pp. 2829-2853 ◽  
Author(s):  
Marouane Temimi ◽  
Ricardo Fonseca ◽  
Narendra Nelli ◽  
Michael Weston ◽  
Mohan Thota ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Soil Texture ◽  
Land Surface ◽  
Surface Wind ◽  
Surface Fluxes ◽  
Abu Dhabi ◽  
Horizontal Wind ◽  
Cold Bias ◽  
Near Surface ◽  
The Impact

AbstractA thorough evaluation of the Weather Research and Forecasting (WRF) Model is conducted over the United Arab Emirates, for the period September 2017–August 2018. Two simulations are performed: one with the default model settings (control run), and another one (experiment) with an improved representation of soil texture and land use land cover (LULC). The model predictions are evaluated against observations at 35 weather stations, radiosonde profiles at the coastal Abu Dhabi International Airport, and surface fluxes from eddy-covariance measurements at the inland city of Al Ain. It is found that WRF’s cold temperature bias, also present in the forcing data and seen almost exclusively at night, is reduced when the surface and soil properties are updated, by as much as 3.5 K. This arises from the expansion of the urban areas, and the replacement of loamy regions with sand, which has a higher thermal inertia. However, the model continues to overestimate the strength of the near-surface wind at all stations and seasons, typically by 0.5–1.5 m s−1. It is concluded that the albedo of barren/sparsely vegetated regions in WRF (0.380) is higher than that inferred from eddy-covariance observations (0.340), which can also explain the referred cold bias. At the Abu Dhabi site, even though soil texture and LULC are not changed, there is a small but positive effect on the predicted vertical profiles of temperature, humidity, and horizontal wind speed, mostly between 950 and 750 hPa, possibly because of differences in vertical mixing.

scholarly journals The Impact of Climate Change on the Value of Growing Maize as a Biofuel

2020 ◽  
Vol 4 (1) ◽  
pp. 13-26
Author(s):  
Sally Olasogba ◽  
Les DUCKERS
Keyword(s):  
Climate Change ◽  
Carbon Footprint ◽  
Carbon Emissions ◽  
Energy Use ◽  
Farm Management ◽  
Energy Output ◽  
Net Energy ◽  
Primary Concern ◽  
Impact Of Climate Change ◽  
The Impact

Abstract: Aim: According to COP23, Climate Change threatens the stability of the planet’s ecosystems, with a tipping point believed to be at only +2°C.  With the burning of fossil fuels, held responsible for the release of much of the greenhouse gases, a sensible world- wide strategy is to replace fossil fuel energy sources with renewable ones. The renewable resources such as wind, hydro, geothermal, wave and tidal energies are found in particular geographical locations whereas almost every country is potentially able to exploit PV and biomass. This paper examines the role that changing climate could have on the growing and processing of biomass. The primary concern is that future climates could adversely affect the yield of crops, and hence the potential contribution of biomass to the strategy to combat climate change. Maize, a C4 crop, was selected for the study because it can be processed into biogas or other biofuels. Four different Nigerian agricultural zones growing maize were chosen for the study. Long-term weather data was available for the four sites and this permitted the modelling of future climates. Design / Research methods: The results of this study come from modelling future climates and applying this to crop models. This unique work, which has integrated climate change and crop modelling to forecast yield and carbon emissions, reveals how maize responds to the predicted increased temperature, change in rainfall, and the variation in weather patterns. In order to fully assess a biomass crop, the full energy cycle and carbon emissions were estimated based on energy and materials inputs involved in farm management: fertilizer application, and tillage type. For maize to support the replacement strategy mentioned above it is essential that the ratio of energy output to energy input exceeds 1, but of course it should be as large as possible. Conclusions / findings: Results demonstrate that the influence of climate change is important and in many scenarios, acts to reduce yield, but that the negative effects can be partially mitigated by careful selection of farm management practices. Yield and carbon footprint is particularly sensitive to the application rate of fertilizer across all locations whilst climate change is the causal driver for the increase in net energy and carbon footprint at most locations. Nonetheless, in order to ensure a successful strategic move towards a low carbon future, and sustainable implementation of biofuel policies, this study provides valuable information for the Nigerian government and policy makers on potential AEZs to cultivate maize under climate change. Further research on the carbon footprint of alternative bioenergy feedstock to assess their environmental carbon footprint and net energy is strongly suggested. Originality / value of the article: This paper extends the review on the impact of climate change on maize production to include future impacts on net energy use and carbon footprint using a fully integrated assessment framework. Most studies focus only on current farm energy use and historical climate change impact on farm GHG emissions.   

scholarly journals Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

2014 ◽  
Vol 11 (5) ◽  
pp. 7685-7719 ◽  
Author(s):  
M. Broich ◽  
A. Huete ◽  
M. G. Tulbure ◽  
X. Ma ◽  
Q. Xin ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Land Management ◽  
Land Surface ◽  
Vegetation Index ◽  
Southern Oscillation ◽  
Quantitative Information ◽  
Decadal Climate Variability ◽  
Eastern Australia ◽  
Phenological Cycle

Abstract. Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually reoccurring patterns. Yet, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e. drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here we focused on Australia, the driest inhabited continent with one of the most variable rainfall climates in the world and vast areas of dryland systems. Detailed and internally consistent studies investigating phenological cycles and their response to climate variability across the entire continent designed specifically for Australian dryland conditions are missing. To fill this knowledge gap and to advance phenological research, we used existing methods more effectively to study geographic and climate-driven variability in phenology over Australia. We linked derived phenological metrics with rainfall and the Southern Oscillation Index (SOI). We based our analysis on Enhanced Vegetation Index (EVI) data from the MODerate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2013, which included extreme drought and wet years. We conducted a continent-wide investigation of the link between phenology and climate variability and a more detailed investigation over the Murray–Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles. Phenological cycle peaks occurred not only during the austral summer but at any time of the year, and their timing varied by more than a month in the interior of the continent. The phenological cycle peak magnitude and integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over north-eastern Australia and within the MDB predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of productivity) showed positive anomalies of more than two standard deviations over most of eastern Australia in 2009–2010, which coincided with the transition between the El Niño induced decadal droughts to flooding caused by La Niña. The quantified spatial-temporal variability in phenology across Australia in response to climate variability presented here provides important information for land management and climate change studies and applications.

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