Anthropogenic land cover change impact on climate extremes during the 21st century

2020 ◽  
Author(s):  
Benjamin Quesada ◽  
Souleymane Sy
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Extreme Events ◽  
Regional Scale ◽  
Extreme Weather ◽  
Northeastern Brazil ◽  
Eastern Africa ◽  
Extreme Weather Events ◽  
Rainfall Extremes ◽  
Global Mean

<p>Beyond global mean temperatures, anthropogenic land cover change (LCC) can have significant impacts at regional and seasonal scales but also on extreme weather events to which human, natural and economical systems are highly vulnerable. However, the effects of LCC on extreme events remain either largely unexplored at global and regional scale and/or without consensus. Here, using several Earth System Models under two different LCC scenarios (the RCP8.5 and RCP2.6 Representative Concentration Pathways) and analyzing 20 extreme weather indices, we find future LCC substantially modulates projected weather extremes particularly at regional level.</p><p>On average by the end of the 21<sup>st</sup> century, under RCP8.5 and RCP2.6 scenarios, future LCC robustly lessens global projections of high rainfall extremes. Accounting for LCC diminishes regional projections of heavy precipitation days or consecutive wet days by more than 50% in southern Africa or northeastern Brazil but intensifies projected dry days in eastern Africa by 30%. LCC do not substantially affect projections of global and regional temperature extremes projections (<5%), but it can impact global rainfall extremes 2.5 times more than global mean rainfall projections.</p><p>Under RCP2.6 scenario, global LCC impacts are similar but of lesser magnitude while at regional scale in Amazon or Asia, LCC enhances drought projections. We investigate the underlying biophysical drivers behind those projected changes.</p><p>We stress here that multi-coupled modelling frameworks incorporating all aspects of land use-land cover change and more model-data benchmarking are needed for reliable projections of extreme events.</p><div> <div> </div> </div>

scholarly journals Effects of land cover change on temperature and rainfall extremes in multi-model ensemble simulations

2012 ◽  
Vol 3 (2) ◽  
pp. 597-641 ◽  
Author(s):  
A. J. Pitman ◽  
N. de Noblet-Ducoudré ◽  
F. B. Avila ◽  
L. V. Alexander ◽  
J.-P. Boisier ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Regional Scale ◽  
Extreme Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Rainfall Extremes ◽  
The Impact ◽  
Change In Mean

Abstract. The impact of historical land use induced land cover change (LULCC) on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes is amplified. While we find some evidence that individual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperature to LULCC is relatively large, we conclude that unless this forcing is included we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.

Extreme Weather and Climate

2017 ◽  
Author(s):  
Friederike Otto
Keyword(s):  
Extreme Events ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Event ◽  
Weather Forecasts ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Weather Events ◽  
Global Mean

Natural disasters and extreme weather events have been of great societal importance throughout history and often brought everyday life to a catastrophic halt, in a way sometimes comparable to wars and epidemics, only without the lead time. Extreme weather events with large impacts serve as an anchor point of the collective memory of the population in the affected area. Every northern German of the right age remembers the storm surge of 1962 and where they were at the time and has friends or family effected by the event. The “dust bowl” of the 1930s with extensive droughts and heat waves shaped the life of a generation in the United States, and the Sahel droughts in the 1960s and 1970s led to famine and dislocation of population on a massive scale the region arguably never quite recovered from. Hurricane Hyian in 2013 is said to have directly influenced the outcome of the annual Conference of the Parties (COP) United Nation Framework Convention for Climate Change Negotiations in Warsaw, leading to the inclusion of a mechanism to deal with loss and damage from climate-related disasters. Though earthquakes are still fairly unpredictable on short timescales, this is not the case for weather events. Weather forecasts today are so good that we normally know the time and location of the landfall of a hurricane within a 100-mile radius days in advance. Improvements in the prediction of slow-onset events such as droughts (which depend on the rainfall over a large region and whole season) are less striking but have still improved dramatically in the late 20th and early 21st centuries. One of the major reasons for the large increase in the accuracy of weather forecasts is the exponential increase in computing power, which allows scientists to predict and study extreme weather events using complex computer models, simulating possible weather events under certain conditions to understand the statistics of and physical mechanisms behind extreme events. Extreme events are by definition rare and thus impossible to understand from historical records of weather observation alone. Despite the progress on our understanding of and ability to predict extreme weather events, substantial uncertainties remain. Two aspects are of particular importance. Firstly, we know that the climate is changing, having observed almost a one-degree increase in global mean temperature. However, global mean temperature doesn’t kill anyone, extreme weather events do. Their frequency and intensity is changing and will continue to change, but the extent of these changes depends on a host of both global and local factors. Secondly, whether or not a rare weather event leads to extreme impacts depends largely on the vulnerability and exposure of the affected societies. If these are high, even a perfectly forecasted weather event leads to disaster.

scholarly journals Effects of land cover change on temperature and rainfall extremes in multi-model ensemble simulations

2012 ◽  
Vol 3 (2) ◽  
pp. 213-231 ◽  
Author(s):  
A. J. Pitman ◽  
N. de Noblet-Ducoudré ◽  
F. B. Avila ◽  
L. V. Alexander ◽  
J.-P. Boisier ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Climate Models ◽  
Regional Scale ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Rainfall Extremes ◽  
The Impact ◽  
Change In Mean

Abstract. The impact of historical land use induced land cover change (LULCC) on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, consistent changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC-induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models, the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes are amplified. While we find some evidence that individual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperatures to LULCC is relatively large, we conclude that unless this forcing is included, we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.

scholarly journals Impacts of compound extreme weather events on ozone in the present and future

2018 ◽  
Author(s):  
Junxi Zhang ◽  
Yang Gao ◽  
Kun Luo ◽  
L. Ruby Leung ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Extreme Events ◽  
Heat Waves ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Low Wind Speed ◽  
Weather Events ◽  
The Us ◽  
The Future ◽  
Compound Events ◽  
Ozone Episodes

Abstract. The Weather Research and Forecasting model with Chemistry (WRF/Chem) was used to study the effect of extreme weather events on ozone in US for historical (2001–2010) and future (2046–2055) periods under RCP8.5 scenario. During extreme weather events, including heat waves, atmospheric stagnation, and their compound events, ozone concentration is much higher compared to non-extreme events period. A striking enhancement of effect during compound events is revealed when heat wave and stagnation occur simultaneously and both high temperature and low wind speed promote the production of high ozone concentrations. In regions with high emissions, compound extreme events can shift the high-end tails of the probability density functions (PDFs) of ozone to even higher values to generate extreme ozone episodes. In regions with low emissions, extreme events can still increase high ozone frequency but the high-end tails of the PDFs are constrained by the low emissions. Despite large anthropogenic emission reduction projected for the future, compound events increase ozone more than the single events by 10 % to 13 %, comparable to the present, and high ozone episodes are not eliminated. Using the CMIP5 multi-model ensemble, the frequency of compound events is found to increase more dominantly compared to the increased frequency of single events in the future over the US, Europe, and China. High ozone episodes will likely continue in the future due to increases in both frequency and intensity of extreme events, despite reductions in anthropogenic emissions of its precursors. However, the latter could reduce or eliminate extreme ozone episodes, so improving projections of compound events and their impacts on extreme ozone may better constrain future projections of extreme ozone episodes that have detrimental effects on human health.

scholarly journals Joining the dots of land-use and land-cover change in Eastern Africa

2018 ◽  
Vol 26 (1) ◽  
pp. 16-17
Author(s):  
Oliver Boles ◽  
C Courtney-Mustaphi ◽  
S Richer ◽  
R Marchant
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Eastern Africa

scholarly journals Extreme weather exposure identification for road networks – a comparative assessment of statistical methods

2017 ◽  
Vol 17 (4) ◽  
pp. 515-531 ◽  
Author(s):  
Matthias Schlögl ◽  
Gregor Laaha
Keyword(s):  
Extreme Events ◽  
Information Gain ◽  
Extreme Weather ◽  
Extreme Value ◽  
Added Value ◽  
Extreme Value Statistics ◽  
Extreme Weather Events ◽  
Mean Residual Life ◽  
Data Set ◽  
Return Levels

Abstract. The assessment of road infrastructure exposure to extreme weather events is of major importance for scientists and practitioners alike. In this study, we compare the different extreme value approaches and fitting methods with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series (PDS) over the standardly used annual maxima series (AMS) in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing PDS) being superior to the block maxima approach (employing AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behavior was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was visible from neither the square-root criterion nor standardly used graphical diagnosis (mean residual life plot) but rather from a direct comparison of AMS and PDS in combined quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best-suited approach. This will make the analyses more robust, not only in cases where threshold selection and dependency introduces biases to the PDS approach but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend the use of conditional performance measures that focus on rare events only in addition to standardly used unconditional indicators. The findings of the study directly address road and traffic management but can be transferred to a range of other environmental variables including meteorological and hydrological quantities.

scholarly journals Assessing the impacts of extreme weather events on potable water quality: the value to managers of a highly participatory, integrated modelling approach

2018 ◽  
Vol 2 (1) ◽  
pp. 9-24
Author(s):  
Edoardo Bertone ◽  
Oz Sahin ◽  
Russell Richards ◽  
Anne Roiko
Keyword(s):  
Water Quality ◽  
Decision Support ◽  
Extreme Events ◽  
Drinking Water Treatment ◽  
Extreme Weather ◽  
Integrated Modelling ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Sd Model

Abstract A decision support tool was created to estimate the treatment efficiency of an Australian drinking water treatment system based on different combinations of extreme weather events and long-term changes. To deal with uncertainties, missing data, and nonlinear behaviours, a Bayesian network (BN) was coupled with a system dynamics (SD) model. The preliminary conceptual structures of these models were developed through stakeholders' consultation. The BN model could rank extreme events, and combinations of them, based on the severity of their impact on health-related water quality. The SD model, in turn, was used to run a long-term estimation of extreme events' impacts by including temporal factors such as increased water demand and customer feedback. The integration of the two models was performed through a combined Monte Carlo–fuzzy logic approach which allowed to take the BN's outputs as inputs for the SD model. The final product is a participatory, multidisciplinary decision support system allowing for robust, sustainable long-term water resources management under uncertain conditions for a specific location.

scholarly journals Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations

2017 ◽  
Vol 14 (22) ◽  
pp. 5053-5067 ◽  
Author(s):  
Wei Li ◽  
Philippe Ciais ◽  
Shushi Peng ◽  
Chao Yue ◽  
Yilong Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Regional Scale ◽  
Model Estimate ◽  
Tropical Regions ◽  
Vegetation Models ◽  
Global Vegetation ◽  
Constraint Approach ◽  
The Impact

Abstract. The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (ELUCc) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and ELUCc. This method is applicable on the global and regional scale. The original DGVM estimates of ELUCc range from 94 to 273 PgC during 1901–2012. After constraining by current biomass observations, we derive a best estimate of 155 ± 50 PgC (1σ Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained ELUCc is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.

scholarly journals Extreme Weather Events and Its Impacts on Rice Production in Coastal Odisha Region of India

2021 ◽  
Author(s):  
S Vijayakumar ◽  
A.K. Nayak ◽  
N. Manikandan ◽  
Suchismita Pattanaik ◽  
Rahul Tripathi ◽  
...  
Keyword(s):  
Grain Yield ◽  
Extreme Events ◽  
Heavy Precipitation ◽  
Extreme Weather ◽  
Significant Trend ◽  
Extreme Weather Events ◽  
Positive Trend ◽  
Rice Grain ◽  
Weather Events ◽  
Precipitation And Temperature

Abstract The study investigates trend in extreme daily precipitation and temperature over coastal Odisha, India. 18 weather indices (8 related to temperature and 10 related to rainfall) were calculated using RClimDex software package for the period 1980–2010 . Trend analysis was carried out using linear regression and non-parametric Mann-Kendall test to find out the statistical significance of various indices. Results indicated, a strong and significant trend in temperature indices while the weak and non-significant trend in precipitation indices. The positive trend in Tmax mean, Tmin mean, TN90p (warm nights), TX90p (warm days), diurnal temperature range (DTR), warm spell duration indicator (WSDI), consecutive dry days (CDD) indicates increasing the frequency of warming events in coastal Odisha. Similarly, positive trend in highest maximum 1-day precipitation (RX1), highest maximum 2 consecutive day precipitation (RX2), highest maximum 3 consecutive day precipitation (RX3), highest maximum 5 consecutive day precipitation (RX5), number of heavy precipitation days (≥64.5mm), number of very heavy precipitation days (≥124.5mm) and negative trend in the number of rainy days (R2.5mm), consecutive wet days (CWD) indicate changes toward the more intense and poor distribution of precipitation in coastal Odisha. The combined effect of precipitation and temperature extreme events showed negative effects on rice grain yield. With the increasing number of extreme events there was sharp decline in rice grain yield was observed in the same year in all the coastal districts. This study emphasizes the need for new technology/management practice to minimize the impacts of extreme weather events on rice yield.

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