scholarly journals Climate Impacts from Afforestation and Deforestation in Europe

2019 ◽  
Vol 23 (1) ◽  
pp. 1-27 ◽  
Author(s):  
G. Strandberg ◽  
E. Kjellström
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Climate Model ◽  
Climate Impacts ◽  
Heat Fluxes ◽  
Maximum Temperature ◽  
Vegetation Changes ◽  
Near Surface ◽  
Local Climate ◽  
Mean Temperature

Abstract Changes in vegetation are known to have an impact on climate via biogeophysical effects such as changes in albedo and heat fluxes. Here, the effects of maximum afforestation and deforestation are studied over Europe. This is done by comparing three regional climate model simulations—one with present-day vegetation, one with maximum afforestation, and one with maximum deforestation. In general, afforestation leads to more evapotranspiration (ET), which leads to decreased near-surface temperature, whereas deforestation leads to less ET, which leads to increased temperature. There are exceptions, mainly in regions with little water available for ET. In such regions, changes in albedo are relatively more important for temperature. The simulated biogeophysical effect on seasonal mean temperature varies between 0.5° and 3°C across Europe. The effect on minimum and maximum temperature is larger than that on mean temperature. Increased (decreased) mean temperature is associated with an even larger increase (decrease) in maximum summer (minimum winter) temperature. The effect on precipitation is found to be small. Two additional simulations in which vegetation is changed in only one-half of the domain were also performed. These simulations show that the climatic effects from changed vegetation in Europe are local. The results imply that vegetation changes have had, and will have, a significant impact on local climate in Europe; the climatic response is comparable to climate change under RCP2.6. Therefore, effects from vegetation change should be taken into account when simulating past, present, and future climate for this region. The results also imply that vegetation changes could be used to mitigate local climate change.

scholarly journals What does global mean temperature tell us about local climate?

2015 ◽  
Vol 373 (2054) ◽  
pp. 20140426 ◽  
Author(s):  
Rowan Sutton ◽  
Emma Suckling ◽  
Ed Hawkins
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Climate Impacts ◽  
Climate Projections ◽  
Local Climate ◽  
Forced Responses ◽  
The Relationship ◽  
Global Mean

The subject of climate feedbacks focuses attention on global mean surface air temperature (GMST) as the key metric of climate change. But what does knowledge of past and future GMST tell us about the climate of specific regions? In the context of the ongoing UNFCCC process, this is an important question for policy-makers as well as for scientists. The answer depends on many factors, including the mechanisms causing changes, the timescale of the changes, and the variables and regions of interest. This paper provides a review and analysis of the relationship between changes in GMST and changes in local climate, first in observational records and then in a range of climate model simulations, which are used to interpret the observations. The focus is on decadal timescales, which are of particular interest in relation to recent and near-future anthropogenic climate change. It is shown that GMST primarily provides information about forced responses, but that understanding and quantifying internal variability is essential to projecting climate and climate impacts on regional-to-local scales. The relationship between local forced responses and GMST is often linear but may be nonlinear, and can be greatly complicated by competition between different forcing factors. Climate projections are limited not only by uncertainties in the signal of climate change but also by uncertainties in the characteristics of real-world internal variability. Finally, it is shown that the relationship between GMST and local climate provides a simple approach to climate change detection, and a useful guide to attribution studies.

scholarly journals Assessment of Local Climate Change: Historical Trends and RCM Multi-Model Projections Over the Salento Area (Italy)

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 978 ◽  
Author(s):  
Marco D’Oria ◽  
Maria Tanda ◽  
Valeria Todaro
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Heat Waves ◽  
Regional Climate ◽  
Warm Season ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Local Climate

This study provides an up-to-date analysis of climate change over the Salento area (southeast Italy) using both historical data and multi-model projections of Regional Climate Models (RCMs). The accumulated anomalies of monthly precipitation and temperature records were analyzed and the trends in the climate variables were identified and quantified for two historical periods. The precipitation trends are in almost all cases not significant while the temperature shows statistically significant increasing tendencies especially in summer. A clear changing point around the 80s and at the end of the 90s was identified by the accumulated anomalies of the minimum and maximum temperature, respectively. The gradual increase of the temperature over the area is confirmed by the climate model projections, at short—(2016–2035), medium—(2046–2065) and long-term (2081–2100), provided by an ensemble of 13 RCMs, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). All the models agree that the mean temperature will rise over this century, with the highest increases in the warm season. The total annual rainfall is not expected to significantly vary in the future although systematic changes are present in some months: a decrease in April and July and an increase in November. The daily temperature projections of the RCMs were used to identify potential variations in the characteristics of the heat waves; an increase of their frequency is expected over this century.

scholarly journals The impact of climate change in wheat and barley yields in the Iberian Peninsula

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Virgílio A. Bento ◽  
Andreia F. S. Ribeiro ◽  
Ana Russo ◽  
Célia M. Gouveia ◽  
Rita M. Cardoso ◽  
...  
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Model ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Historical Period ◽  
Early Winter ◽  
Impact Of Climate Change ◽  
The Impact ◽  
Spring Maximum

AbstractThe impact of climate change on wheat and barley yields in two regions of the Iberian Peninsula is here examined. Regression models are developed by using EURO-CORDEX regional climate model (RCM) simulations, forced by ERA-Interim, with monthly maximum and minimum air temperatures and monthly accumulated precipitation as predictors. Additionally, RCM simulations forced by different global climate models for the historical period (1972–2000) and mid-of-century (2042–2070; under the two emission scenarios RCP4.5 and RCP8.5) are analysed. Results point to different regional responses of wheat and barley. In the southernmost regions, results indicate that the main yield driver is spring maximum temperature, while further north a larger dependence on spring precipitation and early winter maximum temperature is observed. Climate change seems to induce severe yield losses in the southern region, mainly due to an increase in spring maximum temperature. On the contrary, a yield increase is projected in the northern regions, with the main driver being early winter warming that stimulates earlier growth. These results warn on the need to implement sustainable agriculture policies, and on the necessity of regional adaptation strategies.

scholarly journals Simulated climate variability in the region of Rapa Nui during the last millennium

2011 ◽  
Vol 7 (1) ◽  
pp. 381-395 ◽  
Author(s):  
C. Junk ◽  
M. Claussen
Keyword(s):  
Large Scale ◽  
Vegetation Change ◽  
Climate Model ◽  
Volcanic Eruptions ◽  
Easter Island ◽  
Ice Age ◽  
Vegetation Changes ◽  
Last Millennium ◽  
Climate Data ◽  
Climatic Fluctuations

Abstract. Easter Island, an isolated island in the Southeast Pacific, was settled by the Polynesians probably between 600 and 1200 AD and discovered by the Europeans in 1722 AD. While the Polynesians presumably found a profuse palm woodland on Easter Island, the Europeans faced a landscape dominated by grassland. Scientists have examined potential anthropogenic, biological and climatic induced vegetation changes on Easter Island. Here, we analyze observational climate data for the last decades and climate model results for the period 800–1750 AD to explore potential causes for a climatic-induced vegetation change. A direct influence of the ENSO phenomenon on the climatic parameters of Easter Island could not be found in the model simulations. Furthermore, strong climatic trends from a warm Medieval Period to a Little Ice Age or rapid climatic fluctuations due to large volcanic eruptions were not verifiable for the Easter Island region, although they are detectable in the simulations for many regions world wide. Hence we tentatively conclude that large-scale climate changes in the oceanic region around Easter Island might be too small to explain strong vegetation changes on the island over the last millennium.

scholarly journals Diagnosing CO2 emission-induced feedbacks between the Southern Ocean carbon cycle and the climate system: A multiple Earth System Model analysis using a water mass tracking approach

2021 ◽  
pp. 1-62
Author(s):  
Tilla Roy ◽  
Jean Baptiste Sallée ◽  
Laurent Bopp ◽  
Nicolas Metzl
Keyword(s):  
Climate Change ◽  
Southern Ocean ◽  
Water Mass ◽  
Buffering Capacity ◽  
Climate Impacts ◽  
System Model ◽  
Earth System ◽  
Local Climate ◽  
Mode Waters ◽  
Ocean Carbon

AbstractAnthropogenic CO2 emission-induced feedbacks between the carbon cycle and the climate system perturb the efficiency of atmospheric CO2 uptake by land and ocean carbon reservoirs. The Southern Ocean is a region where these feedbacks can be largest and differ most among Earth System Model projections of 21st century climate change. To improve our mechanistic understanding of these feedbacks, we develop an automated procedure that tracks changes in the positions of Southern Ocean water masses and their carbon uptake. In an idealised ensemble of climate change projections, we diagnose two carbon–concentration feedbacks driven by atmospheric CO2 (due to increasing air-sea CO2 partial pressure difference, dpCO2, and reducing carbonate buffering capacity) and two carbon–climate feedbacks driven by climate change (due to changes in the water mass surface outcrop areas and local climate impacts). Collectively these feedbacks increase the CO2 uptake by the Southern Ocean and account for one-fifth of the global uptake of CO2 emissions. The increase in CO2 uptake is primarily dpCO2-driven, with Antarctic intermediate waters making the largest contribution; the remaining three feedbacks partially offset this increase (by ~25%), with maximum reductions in Subantarctic mode waters. The process dominating the decrease in CO2 uptake is water mass-dependent: reduction in carbonate buffering capacity in Subtropical and Subantarctic mode waters, local climate impacts in Antarctic intermediate waters, and reduction in outcrop areas in circumpolar deep waters and Antarctic bottom waters. Intermodel variability in the feedbacks is predominately dpCO2–driven and should be a focus of efforts to constrain projection uncertainty.

scholarly journals Evaluation of three new surface irrigation parameterizations in the WRF-ARW v3.8.1 model: the Po Valley (Italy) case study

2020 ◽  
Vol 13 (7) ◽  
pp. 3179-3201
Author(s):  
Arianna Valmassoi ◽  
Jimy Dudhia ◽  
Silvana Di Sabatino ◽  
Francesco Pilla
Keyword(s):  
Potential Evapotranspiration ◽  
Mediterranean Area ◽  
Maximum Temperature ◽  
Surface Irrigation ◽  
Start Time ◽  
Near Surface ◽  
Local Climate ◽  
Po Valley ◽  
Soil Variables ◽  
Water Amount

Abstract. Irrigation is a method of land management that can affect the local climate. Recent literature shows that it affects mostly the near-surface variables and it is associated with an irrigation cooling effect. However, there is no common parameterization that also accounts for a realistic water amount, and this factor could ascribe one cause to the different impacts found in previous studies. This work aims to introduce three new surface irrigation parameterizations within the WRF-ARW model (v3.8.1) that consider different evaporative processes. The parameterizations are tested on one of the regions where global studies disagree on the signal of irrigation: the Mediterranean area and in particular the Po Valley. Three sets of experiments are performed using the same irrigation water amount of 5.7 mm d−1, derived from Eurostat data. Two complementary validations are performed for July 2015: monthly mean, minimum, and maximum temperature with ground stations and potential evapotranspiration with the MODIS product. All tests show that for both mean and maximum temperature, as well as potential evapotranspiration simulated fields approximate observation-based values better when using the irrigation parameterizations. This study addresses the sensitivity of the results to human-decision assumptions of the parameterizations: start time, length, and frequency. The main impact of irrigation on surface variables such as soil moisture is due to the parameterization choice itself affecting evaporation, rather than the timing. Moreover, on average, the atmosphere and soil variables are not very sensitive to the parameterization assumptions for realistic timing and length.

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

scholarly journals AirClim: an efficient tool for climate evaluation of aircraft technology

2008 ◽  
Vol 8 (16) ◽  
pp. 4621-4639 ◽  
Author(s):  
V. Grewe ◽  
A. Stenke
Keyword(s):  
Climate Change ◽  
Water Vapour ◽  
Radiative Forcing ◽  
Climate Impacts ◽  
Assessment Tools ◽  
Surface Temperature Change ◽  
Near Surface ◽  
Temperature Changes ◽  
Wide Range ◽  
Concentration Changes

Abstract. Climate change is a challenge to society and to cope with requires assessment tools which are suitable to evaluate new technology options with respect to their impact on global climate. Here we present AirClim, a model which comprises a linearisation of atmospheric processes from the emission to radiative forcing, resulting in an estimate in near surface temperature change, which is presumed to be a reasonable indicator for climate change. The model is designed to be applicable to aircraft technology, i.e. the climate agents CO2, H2O, CH4 and O3 (latter two resulting from NOx-emissions) and contrails are taken into account. AirClim combines a number of precalculated atmospheric data with aircraft emission data to obtain the temporal evolution of atmospheric concentration changes, radiative forcing and temperature changes. These precalculated data are derived from 25 steady-state simulations for the year 2050 with the climate-chemistry model E39/C, prescribing normalised emissions of nitrogen oxides and water vapour at various atmospheric regions. The results show that strongest climate impacts (year 2100) from ozone changes occur for emissions in the tropical upper troposphere (60 mW/m2; 80 mK for 1 TgN/year emitted) and from methane changes from emissions in the middle tropical troposphere (−2.7% change in methane lifetime; –30 mK per TgN/year). For short-lived species (e.g. ozone, water vapour, methane) individual perturbation lifetimes are derived depending on the region of emission. A comparison of this linearisation approach with results from a comprehensive climate-chemistry model shows reasonable agreement with respect to concentration changes, radiative forcing, and temperature changes. For example, the total impact of a supersonic fleet on radiative forcing (mainly water vapour) is reproduced within 10%. A wide range of application is demonstrated.

scholarly journals Impacts of Water Consumption in the Haihe Plain on the Climate of the Taihang Mountains, North China

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Jing Zou ◽  
Chesheng Zhan ◽  
Ruxin Zhao ◽  
Peihua Qin ◽  
Tong Hu ◽  
...  
Keyword(s):  
Water Consumption ◽  
Land Surface ◽  
North China ◽  
Climate Model ◽  
Regional Climate ◽  
Heat Fluxes ◽  
Local Climate ◽  
Climatic Impacts ◽  
Cooling Effects ◽  
Surrounding Areas

In this study, the RegCM4 regional climate model was employed to investigate the impacts of water consumption in the Haihe Plain on the local climate in the nearby Taihang Mountains. Four simulation tests of twelve years’ duration were conducted with various schemes of water consumption by residents, industries, and agriculture. The results indicate that water exploitation and consumption in the Haihe Plain causes wetting and cooling of the local land surface and rapid increases in the depth of the groundwater table. These wetting and cooling effects increase atmospheric moisture, which is transported to surrounding areas, including the Taihang Mountains to the west. In a simulation where water consumption in the Haihe Plain was doubled, the wetting and cooling effects in the Taihang Mountains were enhanced but at less than double the amount, because a cooler land surface does not enhance atmospheric convective activities. The impacts of water consumption activities in the Haihe Plain were more obvious during the irrigation seasons (primarily spring and summer). In addition, the land surface variables in the Taihang Mountains, e.g., sensible and latent heat fluxes, were less sensitive to the climatic impacts due to the water consumption activities in the Haihe Plain because they were strongly affected by local surface energy balance.

scholarly journals Climate Impacts of Land-Cover and Land-Use Changes in Tropical Islands under Conditions of Global Climate Change

2013 ◽  
Vol 26 (5) ◽  
pp. 1535-1550 ◽  
Author(s):  
Daniel E. Comarazamy ◽  
Jorge E. González ◽  
Jeffrey C. Luvall ◽  
Douglas L. Rickman ◽  
Robert D. Bornstein
Keyword(s):  
Boundary Layer ◽  
Land Use ◽  
Global Warming ◽  
Land Cover ◽  
Atmospheric Modeling ◽  
Climate Impacts ◽  
Maximum Temperature ◽  
Tropical Islands ◽  
Near Surface ◽  
Convergence Zones

Abstract Land-cover and land-use (LCLU) changes have significant climate impacts in tropical coastal regions with the added complexity of occurring within the context of a warming climate. The individual and combined effects of these two factors in tropical islands are investigated by use of an integrated mesoscale atmospheric modeling approach, taking the northeastern region of Puerto Rico as the test case. To achieve this goal, an ensemble of climate simulations is performed, combining two LCLU and global warming scenarios. Reconstructed agricultural maps and sea surface temperatures form the past (1955–59) scenario, while the present (2000–04) scenario is supported with high-resolution remote sensing LCLU data. Here, the authors show that LCLU changes produced the largest near-surface (2-m AGL) air temperature differences over heavily urbanized regions and that these changes do not penetrate the boundary layer. The influence of the global warming signal induces a positive inland gradient of maximum temperature, possibly because of increased trade winds in the present climatology. These increased winds also generate convergence zones and convection that transport heat and moisture into the boundary layer. In terms of minimum temperatures, the global warming signal induces temperature increases along the coastal plains and inland lowlands.

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