scholarly journals Examining the Interaction of Growing Crops with Local Climate Using a Coupled Crop–Climate Model

2009 ◽  
Vol 22 (6) ◽  
pp. 1393-1411 ◽  
Author(s):  
Tom Osborne ◽  
Julia Slingo ◽  
David Lawrence ◽  
Tim Wheeler
Keyword(s):  
Climate Variability ◽  
Crop Yield ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Crop Growth ◽  
Surface Fluxes ◽  
Near Surface ◽  
Local Climate ◽  
The Impact

Abstract This paper examines to what extent crops and their environment should be viewed as a coupled system. Crop impact assessments currently use climate model output offline to drive process-based crop models. However, in regions where local climate is sensitive to land surface conditions more consistent assessments may be produced with the crop model embedded within the land surface scheme of the climate model. Using a recently developed coupled crop–climate model, the sensitivity of local climate, in particular climate variability, to climatically forced variations in crop growth throughout the tropics is examined by comparing climates simulated with dynamic and prescribed seasonal growth of croplands. Interannual variations in land surface properties associated with variations in crop growth and development were found to have significant impacts on near-surface fluxes and climate; for example, growing season temperature variability was increased by up to 40% by the inclusion of dynamic crops. The impact was greatest in dry years where the response of crop growth to soil moisture deficits enhanced the associated warming via a reduction in evaporation. Parts of the Sahel, India, Brazil, and southern Africa were identified where local climate variability is sensitive to variations in crop growth, and where crop yield is sensitive to variations in surface temperature. Therefore, offline seasonal forecasting methodologies in these regions may underestimate crop yield variability. The inclusion of dynamic crops also altered the mean climate of the humid tropics, highlighting the importance of including dynamical vegetation within climate models.

scholarly journals Flux Replacement as a Method to Diagnose Coupled Land–Atmosphere Model Feedback

2004 ◽  
Vol 5 (6) ◽  
pp. 1034-1048 ◽  
Author(s):  
Paul A. Dirmeyer ◽  
Mei Zhao
Keyword(s):  
Surface State ◽  
Land Surface ◽  
Climate Model ◽  
Initial Conditions ◽  
Systematic Errors ◽  
Surface Fluxes ◽  
Boreal Summer ◽  
Near Surface ◽  
Soil Wetness ◽  
The Impact

Abstract The potential role of the land surface state in improving predictions of seasonal climate is investigated with a coupled land–atmosphere climate model. Climate simulations for 18 boreal-summer seasons (1982–99) have been conducted with specified observed sea surface temperature (SST). The impact on prediction skill of the initial land surface state (interannually varying versus climatological soil wetness) and the effect of errors in downward surface fluxes (precipitation and longwave/shortwave radiation) over land are investigated with a number of parallel experiments. Flux errors are addressed by replacing the downward fluxes with observed values in various combinations to ascertain the separate roles of water and energy flux errors on land surface state variables, upward water and energy fluxes from the land surface, and the important climate variables of precipitation and near-surface air temperature. Large systematic errors are found in the model, which are only mildly alleviated by the specification of realistic initial soil wetness. The model shows little skill in simulating seasonal anomalies of precipitation, but it does have skill in simulating temperature variations. Replacement of the downward surface fluxes has a clear positive impact on systematic errors, suggesting that the land–atmosphere feedback is helping to exacerbate climate drift. Improvement in the simulation of year-to-year variations in climate is even more evident. With flux replacement, the climate model simulates temperature anomalies with considerable skill over nearly all land areas, and a large fraction of the globe shows significant skill in the simulation of precipitation anomalies. This suggests that the land surface can communicate climate anomalies back to the atmosphere, given proper meteorological forcing. Flux substitution appears to have the largest benefit to improving precipitation skill over the Northern Hemisphere midlatitudes, whereas use of realistic land surface initial conditions improves skill to significant levels over regions of the Southern Hemisphere. Correlations between sets of integrations show that the model has a robust and systematic global response to SST anomalies.

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 Modeling the Effect of Desert Urbanization on Local Climate and Natural Dust Generation: A Case Study for Erbil, Iraq

Urban Science ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 46
Author(s):  
Sherzad T. Tahir ◽  
Huei-Ping Huang
Keyword(s):  
Land Surface ◽  
Numerical Models ◽  
Surface Wind ◽  
Surface Fluxes ◽  
Slight Reduction ◽  
Rapid Urbanization ◽  
Near Surface ◽  
Local Climate ◽  
Surface Dust ◽  
Dust Generation

This study uses a suite of meteorological and land-surface models to quantify the changes in local climate and surface dust fluxes associated with desert urbanization. Formulas connecting friction velocity and soil moisture to dust generation are used to quantify surface fluxes for natural wind-blown dust. The models are used to conduct a series of simulations for the desert city of Erbil across a period of rapid urbanization. The results show significant nighttime warming and weak but robust daytime cooling associated with desert urbanization. A slight reduction in near-surface wind speed is also found in the areas undergoing urbanization. These findings are consistent with previous empirical and modeling studies on other desert cities. Numerical models and empirical formulas are used to produce climatological maps of surface dust fluxes as a function of season, and for the pre- and post-urbanization eras. This framework can potentially be used to bridge the gap in observation on the trends in local dust generation associated with land-use changes and urban expansions.

scholarly journals Complexity of Snow Schemes in a Climate Model and Its Impact on Surface Energy and Hydrology

2012 ◽  
Vol 13 (2) ◽  
pp. 521-538 ◽  
Author(s):  
Emanuel Dutra ◽  
Pedro Viterbo ◽  
Pedro M. A. Miranda ◽  
Gianpaolo Balsamo
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Climate Models ◽  
Climate Model ◽  
Single Layer ◽  
Atmospheric Temperature ◽  
Soil Freezing ◽  
Near Surface ◽  
Northern Latitudes ◽  
Tightly Coupled

Abstract Three different complexity snow schemes implemented in the ECMWF land surface scheme Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) are evaluated within the EC-EARTH climate model. The snow schemes are (i) the original HTESSEL single-bulk-layer snow scheme, (ii) a new snow scheme in operations at ECMWF since September 2009, and (iii) a multilayer version of the previous. In offline site simulations, the multilayer scheme outperforms the single-layer schemes in deep snowpack conditions through its ability to simulate sporadic melting events thanks to the lower thermal inertial of the uppermost layer. Coupled atmosphere–land/snow simulations performed by the EC-EARTH climate model are validated against remote sensed snow cover and surface albedo. The original snow scheme has a systematic early melting linked to an underestimation of surface albedo during spring that was partially reduced with the new snow schemes. A key process to improve the realism of the near-surface atmospheric temperature and at the same time the soil freezing is the thermal insulation of the snowpack (tightly coupled with the accuracy of snow mass and density simulations). The multilayer snow scheme outperforms the single-layer schemes in open deep snowpack (such as prairies or tundra in northern latitudes) and is instead comparable in shallow snowpack conditions. However, the representation of orography in current climate models implies limitations for accurately simulating the snowpack, particularly over complex terrain regions such as the Rockies and the Himalayas.

scholarly journals An observation-constrained multi-physics WRF ensemble for simulating European mega heat waves

2015 ◽  
Vol 8 (7) ◽  
pp. 2285-2298 ◽  
Author(s):  
A. I. Stegehuis ◽  
R. Vautard ◽  
P. Ciais ◽  
A. J. Teuling ◽  
D. G. Miralles ◽  
...  
Keyword(s):  
Heat Wave ◽  
Land Surface ◽  
Western Europe ◽  
Climate Models ◽  
Climate Model ◽  
Heat Waves ◽  
Regional Climate ◽  
Shortwave Radiation ◽  
Cold Bias ◽  
The Impact

Abstract. Many climate models have difficulties in properly reproducing climate extremes, such as heat wave conditions. Here we use the Weather Research and Forecasting (WRF) regional climate model with a large combination of different atmospheric physics schemes, in combination with the NOAH land-surface scheme, with the goal of detecting the most sensitive physics and identifying those that appear most suitable for simulating the heat wave events of 2003 in western Europe and 2010 in Russia. In total, 55 out of 216 simulations combining different atmospheric physical schemes have a temperature bias smaller than 1 °C during the heat wave episodes, the majority of simulations showing a cold bias of on average 2–3 °C. Conversely, precipitation is mostly overestimated prior to heat waves, and shortwave radiation is slightly overestimated. Convection is found to be the most sensitive atmospheric physical process impacting simulated heat wave temperature across four different convection schemes in the simulation ensemble. Based on these comparisons, we design a reduced ensemble of five well performing and diverse scheme configurations, which may be used in the future to perform heat wave analysis and to investigate the impact of climate change during summer in Europe.

scholarly journals The regional impact of urban emissions on air quality in Europe: the role of the urban canopy effects

2021 ◽  
Vol 21 (18) ◽  
pp. 14309-14332
Author(s):  
Peter Huszar ◽  
Jan Karlický ◽  
Jana Marková ◽  
Tereza Nováková ◽  
Marina Liaskoni ◽  
...  
Keyword(s):  
Air Quality ◽  
Land Surface ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Scale ◽  
Urban Canopy ◽  
Regional Climate Models ◽  
Near Surface ◽  
Urban Emissions ◽  
The Impact

Abstract. Urban areas are hot spots of intense emissions, and they influence air quality not only locally but on a regional or even global scale. The impact of urban emissions over different scales depends on the dilution and chemical transformation of the urban plumes which are governed by the local- and regional-scale meteorological conditions. These are influenced by the presence of urbanized land surface via the so-called urban canopy meteorological forcing (UCMF). In this study, we investigate for selected central European cities (Berlin, Budapest, Munich, Prague, Vienna and Warsaw) how the urban emission impact (UEI) is modulated by the UCMF for present-day climate conditions (2015–2016) using two regional climate models, the regional climate models RegCM and Weather Research and Forecasting model coupled with Chemistry (WRF-Chem; its meteorological part), and two chemistry transport models, Comprehensive Air Quality Model with Extensions (CAMx) coupled to either RegCM and WRF and the “chemical” component of WRF-Chem. The UCMF was calculated by replacing the urbanized surface by a rural one, while the UEI was estimated by removing all anthropogenic emissions from the selected cities. We analyzed the urban-emission-induced changes in near-surface concentrations of NO2, O3 and PM2.5. We found increases in NO2 and PM2.5 concentrations over cities by 4–6 ppbv and 4–6 µg m−3, respectively, meaning that about 40 %–60 % and 20 %–40 % of urban concentrations of NO2 and PM2.5 are caused by local emissions, and the rest is the result of emissions from the surrounding rural areas. We showed that if UCMF is included, the UEI of these pollutants is about 40 %–60 % smaller, or in other words, the urban emission impact is overestimated if urban canopy effects are not taken into account. In case of ozone, models due to UEI usually predict decreases of around −2 to −4 ppbv (about 10 %–20 %), which is again smaller if UCMF is considered (by about 60 %). We further showed that the impact on extreme (95th percentile) air pollution is much stronger, and the modulation of UEI is also larger for such situations. Finally, we evaluated the contribution of the urbanization-induced modifications of vertical eddy diffusion to the modulation of UEI and found that it alone is able to explain the modeled decrease in the urban emission impact if the effects of UCMF are considered. In summary, our results showed that the meteorological changes resulting from urbanization have to be included in regional model studies if they intend to quantify the regional footprint of urban emissions. Ignoring these meteorological changes can lead to the strong overestimation of UEI.

scholarly journals Large sensitivity of a Greenland ice sheet model to atmospheric forcing fields

2012 ◽  
Vol 6 (2) ◽  
pp. 1037-1083 ◽  
Author(s):  
A. Quiquet ◽  
H. J. Punge ◽  
C. Ritz ◽  
X. Fettweis ◽  
M. Kageyama ◽  
...  
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Global Climate Models ◽  
Near Surface ◽  
Temperature And Precipitation ◽  
The Impact

Abstract. The prediction of future climate and ice sheet evolution requires coupling of ice sheet and climate models. Before proceeding to a coupled setup, we propose to analyze the impact of model simulated climate on an ice sheet. Here, we undertake this exercise for a set of regional and global climate models. Modelled near surface air temperature and precipitation are provided as upper boundary condition to the GRISLI (GRenoble Ice Shelf and Land Ice model) hybrid ice sheet model (ISM) in its Greenland configuration. After 20 kyr of simulation, the resulting ice sheets highlight the differences between the climate models. While modelled ice sheet sizes are generally comparable to the observed ones, there are considerable deviations among the ice sheets on regional scales. These can be explained by difficulties in modelling local temperature and precipitation near the coast. This is especially true in the case of global models. But the deviations of each climate model are also due to the differences in the atmospheric general circulation. In the context of coupling ice sheet and climate models, we conclude that appropriate downscaling methods will be needed and systematic corrections of the climatic variables at the interface may be required in some cases to obtain realistic results for the Greenland ice sheet (GIS).

scholarly journals Risk and reward of the global truffle sector under predicted climate change

2022 ◽  
Author(s):  
Tomas Cejka ◽  
Elizabeth Isaac ◽  
Daniel Oliach ◽  
Fernando Martinez-Pena ◽  
Simon Egli ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Heat Waves ◽  
Global Climate Models ◽  
Future Climate Change ◽  
Production Rates ◽  
Price Levels ◽  
The Impact

Abstract Climate change has been described as the main threat for the cultivation and growth of truffles, but hydroclimate variability and model uncertainty challenge regional projections and adaptation strategies of the emerging sector. Here, we conduct a literature review to define the main Périgord truffle growing regions around the world and use 20 global climate models to assess the impact of future trends and extremes in temperature, precipitation and soil moisture on truffle production rates and price levels in all cultivation regions in the Americas, Europe, South Africa, and Australasia. Climate model simulations project 2.3 million km2 of suitable land for truffle growth will experience 50% faster aridification than the rests of the global land surface, with significantly more heat waves between 2070 and 2099 CE. Overall, truffle production rates will decrease by ~15%, while associated price levels will increase by ~36%. At the same time, a predicted increase in summer precipitation and less intense warming over Australasia will likely alleviate water scarcity and support higher yields of more affordable truffles. Our findings are relevant for truffle farmers and businesses to adapt their irrigation systems and management strategies to future climate change.

scholarly journals An observation-constrained multi-physics RCM ensemble for simulating European mega-heatwaves

2014 ◽  
Vol 7 (6) ◽  
pp. 7861-7886 ◽  
Author(s):  
A. I. Stegehuis ◽  
R. Vautard ◽  
P. Ciais ◽  
A. J. Teuling ◽  
D. G. Miralles ◽  
...  
Keyword(s):  
Land Surface ◽  
Western Europe ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Extremes ◽  
Poor Performance ◽  
Wave Radiation ◽  
Cold Bias ◽  
The Impact

Abstract. Climate models are often not evaluated or calibrated against observations of past climate extremes, resulting in poor performance during for instance heatwave conditions. Here we use the Weather Research and Forecasting (WRF) regional climate model with a large combination of different atmospheric physics schemes, with the goal of detecting the most sensitive ones and identifying those that appear most suitable for simulating the heatwave events of 2003 in Western Europe and 2010 in Russia. 55 Out of 216 simulations combining different atmospheric physical schemes can reproduce the extreme temperatures observed during heatwaves, the majority of simulations showing a cold bias of on average 2–3 °C. Conversely, precipitation is mostly overestimated prior to heatwaves, and short wave radiation is slightly underestimated. Convection is found to be the most sensitive process impacting simulated heatwave temperature, across 4 different convection schemes in the simulation ensemble. Based on these comparisons, we design a reduced ensemble of five well performing and diverse scheme combinations, which may be used in the future to perform heatwave analysis and to investigate the impact of climate change in summer in Europe. Future studies could include the use of different land surface models together with varied physics scheme.

scholarly journals Sensitivity of Land Surface Simulations to the Treatment of Vegetation Properties and the Implications for Seasonal Climate Prediction

2008 ◽  
Vol 9 (3) ◽  
pp. 348-366 ◽  
Author(s):  
Xiang Gao ◽  
Paul A. Dirmeyer ◽  
Zhichang Guo ◽  
Mei Zhao
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Fluxes ◽  
Surface Model ◽  
Canopy Interception ◽  
Area Index ◽  
Temperature And Precipitation ◽  
The Impact

Abstract A coupled land–atmosphere climate model is used to investigate the impact of vegetation parameters (leaf area index, absorbed radiation, and greenness fraction) on the simulation of surface fluxes and their potential role in improving climate forecasts. Ensemble simulations for 1986–95 have been conducted with specified observed sea surface temperatures. The vegetation impact is analyzed by comparing integrations with two different ways of specifying vegetation boundary conditions: observed interannually varying vegetation versus the climatological annual cycle. Parallel integrations are also implemented and analyzed for the land surface model in an uncoupled mode within the framework of the Second Global Soil Wetness Project (GSWP-2) for the same period. The sensitivity to vegetation anomalies in the coupled simulations appears to be relatively small. There appears to be only episodic and localized favorable impacts of vegetation variations on the skill of precipitation and temperature simulations. Impacts are sometimes manifested strictly through changes in land surface fluxes, and in other cases involve clear interactions with atmospheric processes. In general, interannual variations of vegetation tend to increase the temporal variability of radiation fluxes, soil evaporation, and canopy interception loss in terms of both spatial frequency and global mean. Over cohesive regions of significant and persistent vegetation anomalies, cumulative statistics do show a net response of surface fluxes, temperature, and precipitation with vegetation anomalies of ±20% corresponding to a precipitation response of about ±6%. However, in about half of these cases no significant response was found. The results presented here suggest that vegetation may be a useful element of the land surface for enhancing seasonal predictability, but its role in this model appears to be relatively minor. Improvement does not occur in all circumstances, and strong anomalies have the best chance of a positive impact on the simulation.

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