Constructing Site-Specific Climate Change Scenarios on a Monthly Scale Using Statistical Downscaling

2000 ◽  
Vol 66 (1-2) ◽  
pp. 13-27 ◽  
Author(s):  
R. Huth ◽  
J. Kyselý
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Change Scenarios ◽  
Site Specific ◽  
Monthly Scale

A Weather Generator-Based Statistical Downscaling Tool for Site-Specific Assessment of Climate Change Impacts

2018 ◽  
Vol 61 (3) ◽  
pp. 977-993 ◽  
Author(s):  
Jie Chen ◽  
Xunchang John Zhang ◽  
Xiangquan Li
Keyword(s):  
Climate Change ◽  
Soil Erosion ◽  
Statistical Downscaling ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Weather Generator ◽  
Climate Change Scenarios ◽  
Widespread Application ◽  
Site Specific ◽  
Downscaling Method

Abstract. Statistical downscaling approaches are usually used to bridge the gap between climate model outputs and data requirements of impact models such as crop and soil erosion models. This study synthesizes, integrates, and standardizes a statistical downscaling method that was initially developed in 2005 and subsequently evaluated and improved during the last decade. A new downscaling software program, Generator for Point Climate Change (GPCC), has been developed to automate and visualize the method to assist end users with detailed technical and user documentation. GPCC readily generates daily time series of climate change scenarios for local and site-specific climate change impact studies using monthly projections from global climate models or regional climate models. The downscaled variables include precipitation and maximum and minimum temperatures. This software provides a simple but effective climate downscaling tool for assessing the impacts of climate change on crop production, soil hydrology, and soil erosion at a field scale. The tool can also provide an alternative downscaling method to facilitate the international collaborative efforts of the Agricultural Model Intercomparison and Improvement Project (AgMIP) for simulation of world food production and food security assessment. The detailed downscaling methods, their scientific bases, and the advantages of GPCC over other commonly used downscaling methods are presented. GPCC is written in the Matlab language, and a standalone version can be run on Windows XP or above without Matlab software. The tool has a graphical user interface that is simple and easy to generate downscaled climates as well as to visualize downscaled outputs. Each interface tab and key button and their functions are described to facilitate its widespread application. Keywords: Agricultural system models, Climate change impacts, Generator for point climate change, Interface, Statistical downscaling, Stochastic weather generator.

Verification of GCM-Generated Regional Seasonal Precipitation for Current Climate and of Statistical Downscaling Estimates under Changing Climate Conditions

1999 ◽  
Vol 12 (1) ◽  
pp. 258-272 ◽  
Author(s):  
Aristita Busuioc ◽  
Hans von Storch ◽  
Reiner Schnur
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Regional Scale ◽  
The Other ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Seasonal Precipitation ◽  
Empirical Downscaling

Abstract Empirical downscaling procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a downscaling technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical downscaling procedures in climate change applications. The case considered is regional seasonal precipitation in Romania. The downscaling model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). The climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in “time-slice” mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050. The downscaling model is skillful for all seasons except spring. The general features of the large-scale SLP variability are reproduced fairly well by both GCMs in all seasons. The climate models reproduce the empirically determined precipitation–SLP link in winter, whereas the observed link is only partially captured for the other seasons. Thus, these models may be considered skillful with respect to regional precipitation during winter, and partially during the other seasons. Generally, applications of statistical downscaling to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 × CO2 GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical downscaling. Since the skill of the GCMs in regional terms is already established, it is concluded that the downscaling technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.

Impacts of potential climate change on selected agroclimatic indices in Atlantic Canada

2005 ◽  
Vol 85 (2) ◽  
pp. 329-343 ◽  
Author(s):  
A. Bootsma ◽  
S. Gameda and D.W. McKenney
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Growing Degree Days ◽  
Climate Change Scenarios ◽  
Degree Days ◽  
Atlantic Region ◽  
Water Deficits ◽  
Heat Units ◽  
Agricultural Areas ◽  
The Impact

Agroclimatic indices (heat units and water deficits) were determined for the Atlantic region of Canada for a baseline climate (1961 to 1990 period) and for two future time periods (2010 to 2039 and 2040 to 2069). Climate scenarios for the future periods were primarily based on outputs from the Canadian General Circulation Model (GCM) that included the effects of aerosols (CGCMI-A), but variability introduced by multiple GCM experiments was also examined. Climatic data for all three periods were interpolated to a grid of about 10 to 15 km. Agroclimatic indices were computed and mapped based on the gridded data. Based on CGCMI-A scenarios interpolated to the fine grid, average crop heat units (CHU) would increase by 300 to 500 CHU for the 2010 to 2039 period and by 500 to 700 CHU for the 2040 to 2069 period in the main agricultural areas of the Atlantic region. However, increases in CHU for the 2040 to 2069 period typically varied from 450 to 1650 units in these regions when variability among GCM experiments was considered, resulting in a projected range of 2650 to 4000 available CHU. Effective growing degree-days above 5°C (EGDD) typically increased by about 400 units for the 2040 to 2069 period in the main agricultural areas, resulting in available EGDD from 1800 to over 2000 units. Uncertainty introduced by multiple GCMs increased the range from 1700 to 2700 EGDD. A decrease in heat units (cooling) is anticipated along part of the coast of Labrador. Anticipated changes in water deficits (DEFICIT), defined as the amount by which potential evapotranspiration exceeded precipitation over the growing season, typically ranged from +50 to −50 mm for both periods, but this range widened from +50 to −100 mm when variability among GCM experiments was considered. The greatest increases in deficits were expected in the central region of New Brunswick for the 2040 to 2069 period. Our interpolation procedures estimated mean winter and summer temperature changes that were 1.4°C on average lower than a statistical downscaling procedure (SDSM) for four locations. Increases in precipitation during summer and autumn averaged 20% less than SDSM. During periods when SDSM estimated relatively small changes in temperature or precipitation, our interpolation procedure tended to produce changes that were larger than SDSM. Additional investigations would be beneficial that explore the impact of a range of scenarios from other GCM models, other downscaling methods and the potential effects of change in climate variability on these agroclimatic indices. Potential impacts of these changes on crop yields and production in the region also need to be explored. Key words: Crop heat units, effective growing degree-days, water deficits, climate change scenarios, statistical downscaling, spatial interpolation

Same soil - different climate: crop model inter-comparison with lysimeter data of translocated monoliths

2021 ◽  
Author(s):  
Jannis Groh ◽  
Horst H. Gerke ◽  
Keyword(s):  
Climate Change ◽  
Model Performance ◽  
Predictive Ability ◽  
Crop Model ◽  
Climatic Conditions ◽  
Climate Change Scenarios ◽  
Soil Ecosystem ◽  
Site Specific ◽  
Climate Conditions ◽  
Crop Models

<p>Crop model comparisons have mostly been carried out to test predictive ability under previous climate conditions and for soils of the same location. However, the ability of individual agricultural models to predict the effects of changes in climatic conditions on soil-ecosystems beyond the range of site-specific variability is unknown. The objective of this study was to test the predictive ability of agroecosystem models using weighable lysimeter data for the same soil under changing climatic conditions and to compare simulated plant growth and soil-ecosystem response to climate change between these models. To achieve this, data from the TERENO-SOILCan lysimeters-network for a soil-ecosystem at the original site (Dedelow) and data from the lysimeters with Dedelow soil monoliths transferred to Bad Lauchstädt and Selhausen were analysed. The transfer of the soils took place to a drier and warmer location (Bad Lauchstädt) and to a warmer and wetter location (Selhausen) compared to the original location of the soils in Dedelow with the same crop rotation. After model calibration for data from the original Dedelow site, crop growth and soil water balances of transferred Dedelow soil monoliths were predicted using the site-specific boundary conditions and compared with the observations at Selhausen and Bad Lauchstädt. The overall simulation output of the models was separated into a plant-related part, ecosystem-productivity (grain yield, biomass, LAI) and an environmental part, ecosystem-fluxes (evapotranspiration, net-drainage, soil moisture). The results showed that when the soil was transferred to a drier region, the agronomic part of the crop models predicted well, and when the soil was moved to wetter regions, the environmental flow part of the models seemed to predict better. The results suggest that accounting for climate change scenarios, more consideration of soil properties and testing model performance for conditions outside the calibrated range and site-specific variability will help improve the models.</p>

Synoptic-Statistical Approach to Regional Downscaling of IPCC Twenty-First-Century Climate Projections: Seasonal Rainfall over the Hawaiian Islands*

2009 ◽  
Vol 22 (16) ◽  
pp. 4261-4280 ◽  
Author(s):  
Oliver Timm ◽  
Henry F. Diaz
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Hawaiian Islands ◽  
Dry Season ◽  
First Century ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Wet Season ◽  
Near Surface ◽  
Twenty First Century

Abstract A linear statistical downscaling technique is applied to the projection of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) climate change scenarios onto Hawaiian rainfall for the late twenty-first century. Hawaii’s regional rainfall is largely controlled by the strength of the trade winds. During the winter months, disturbances in the westerlies can produce heavy rainfall throughout the islands. A diagnostic analysis of sea level pressure (SLP), near-surface winds, and rainfall measurements at 134 weather observing stations around the islands characterize the correlations between the circulation and rainfall during the nominal wet season (November–April) and dry season (May–October). A comparison of the base climate twentieth-century AR4 model simulations with reanalysis data for the period 1970–2000 is used to define objective selection criterion for the AR4 models. Six out of 21 available models were chosen for the statistical downscaling. These were chosen on the basis of their ability to more realistically simulate the modern large-scale circulation fields in the Hawaiian Islands region. For the AR4 A1B emission scenario, the six analyzed models show important changes in the wind fields around Hawaii by the late twenty-first century. Two models clearly indicate opposite signs in the anomalies. One model projects 20%–30% rainfall increase over the islands; the other model suggests a rainfall decrease of about 10%–20% during the wet season. It is concluded from the six-model ensemble that the most likely scenario for Hawaii is a 5%–10% reduction of the wet-season precipitation and a 5% increase during the dry season, as a result of changes in the wind field. The authors discuss the sources of uncertainties in the projected rainfall changes and consider future improvements of the statistical downscaling work and implications for dynamical downscaling methods.

scholarly journals Evaluating the Impact of Climate Change on Water Productivity of Maize in the Semi-Arid Environment of Punjab, Pakistan

2020 ◽  
Vol 12 (9) ◽  
pp. 3905
Author(s):  
Muhammad Mohsin Waqas ◽  
Syed Hamid Hussain Shah ◽  
Usman Khalid Awan ◽  
Muhammad Waseem ◽  
Ishfaq Ahmad ◽  
...  
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Water Productivity ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Farming Practices ◽  
Impact Of Climate Change ◽  
Swap Model ◽  
Semi Arid

Impact assessments on climate change are essential for the evaluation and management of irrigation water in farming practices in semi-arid environments. This study was conducted to evaluate climate change impacts on water productivity of maize in farming practices in the Lower Chenab Canal (LCC) system. Two fields of maize were selected and monitored to calibrate and validate the model. A water productivity analysis was performed using the Soil–Water–Atmosphere–Plant (SWAP) model. Baseline climate data (1980–2010) for the study site were acquired from the weather observatory of the Pakistan Meteorological Department (PMD). Future climate change data were acquired from the Hadley Climate model version 3 (HadCM3). Statistical downscaling was performed using the Statistical Downscaling Model (SDSM) for the A2 and B2 scenarios of HadCM3. The water productivity assessment was performed for the midcentury (2040–2069) scenario. The maximum increase in the average maximum temperature (Tmax) and minimum temperature (Tmin) was found in the month of July under the A2 and B2 scenarios. The scenarios show a projected increase of 2.8 °C for Tmax and 3.2 °C for Tmin under A2 as well as 2.7 °C for Tmax and 3.2 °C for Tmin under B2 for the midcentury. Similarly, climate change scenarios showed that temperature is projected to decrease, with the average minimum and maximum temperatures of 7.4 and 6.4 °C under the A2 scenario and 7.7 and 6.8 °C under the B2 scenario in the middle of the century, respectively. However, the highest precipitation will decrease by 56 mm under the A2 and B2 scenarios in the middle of the century for the month of September. The input and output data of the SWAP model were processed in R programming for the easy working of the model. The negative impact of climate change was found under the A2 and B2 scenarios during the midcentury. The maximum decreases in Potential Water Productivity (WPET) and Actual Water Productivity (WPAI) from the baseline period to the midcentury scenario of 1.1 to 0.85 kgm−3 and 0.7 to 0.56 kgm−3 were found under the B2 scenario. Evaluation of irrigation practices directs the water managers in making suitable water management decisions for the improvement of water productivity in the changing climate.

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