High-resolution climate projections for South Asia to inform climate impacts and adaptation studies in the Ganges-Brahmaputra-Meghna and Mahanadi deltas

2019 ◽  
Vol 650 ◽  
pp. 1499-1520 ◽  
Author(s):  
Tamara Janes ◽  
Fintan McGrath ◽  
Ian Macadam ◽  
Richard Jones
Keyword(s):  
High Resolution ◽  
South Asia ◽  
Climate Impacts ◽  
Climate Projections ◽  
Adaptation Studies ◽  
The Ganges

scholarly journals The new high resolution climate projections dataset DRIAS-2020 over France

2021 ◽  
Author(s):  
Jean-Michel Soubeyroux ◽  
Sebastien Bernus ◽  
Lola Corre ◽  
Viviane Gouget ◽  
Maryvonne Kerdoncuff ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Projections ◽  
Adaptation Studies ◽  
The Past ◽  
Geographical Variations ◽  
Northern Half ◽  
Climate Simulations ◽  
Climate Service ◽  
Scientific Report

<p><span>This </span><span>communication will </span><span>present the </span><span>new </span><span>high resolution</span><span> climate </span><span>dataset</span><span> over France </span><span>named DRIAS-2020, available</span><span> on the </span><span>French </span><span>partnership </span><span>national </span><span>climate service </span><span>DRIAS</span><span> (Meteo-France, IPSL and CERFACS) </span><span>and </span><span>the associated</span> <span>report</span> <span>published on January 2021.</span></p><p><span>As for the previous </span><span>publication in</span><span> 2014, the climate projections are based on the Euro-Cordex ensemble, whose contents have been greatly enriched over the past six years. Different selection criteria were defined to build a robust and synthetic set (8 to 12 simulations for each of the three scenarios RCP2.6, RCP4.5 and RCP8.5) that best represents the uncertainties of climate change in France. The selected climate simulations were corrected by the new Adamont method </span><span>(Verfaillie et al, 2017) applied to the SAFRAN reanalysis at 8km resolution over France</span><span>. This method provides the DRIAS portal </span><span>(www.drias-climat.fr) </span><span>with a new coherent dataset of several meteorological variables (temperature, precipitation, snow, humidity, wind, radiation). </span></p><p><span>The availability of this dataset was joined </span><span>with</span><span> a scientific report “DRIAS-2020” analysing the expected climate change in France during the 21</span><sup><span>st</span></sup><span> century.</span></p><p><span>The </span><span>mean</span><span> temperature is </span><span>increasing</span><span> for all three scenarios, with a continuous rise until the end of the century (period 2071-2100) for RCP4.5 and RCP8.5, with median values reaching +2.1°C and +3.9°C respectively. This warming, more marked in the summer, presents a geographical variability with a stronger increase in the east of the country. This change in temperature is also reflected in the extremes, with a </span><span>dramatic</span> <span>rise</span><span> in the number of heat wave days in all three scenarios. </span></p><p><span>The evolution of annual precipitation</span><span>, stable or slightly increasing depending on the horizons and scenarios, is accompanied by model uncertainty, which can reverse the sign of the trend. This evolution is subject to seasonal (increase in winter, decrease in summer) and geographical variations (increase in the northern half and decrease in </span><span>some</span><span> regions of the </span><span>South</span><span>). The evolution of extreme precipitation and summer droughts also presents strong uncertainties.</span></p><p><span>These data are intended to be widely used in France for all impact or adaptation studies</span> <span>such as </span><span>already done for </span><span>snow cover </span><span>(ClimSnow) </span><span>or </span><span>in progress for </span><span>water resource (National Explore2 project).</span></p>

STAR-ESDM: A New Bias Correction Method for High-Resolution Station- and Grid-Based Climate Projections

2021 ◽  
Author(s):  
Katharine Hayhoe ◽  
Anne Marie Stoner ◽  
Ian Scott-Fleming ◽  
Hamed Ibrahim
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Heat Waves ◽  
Correction Method ◽  
Climate Impacts ◽  
Daily Maximum ◽  
Climate Projections ◽  
Temperature And Precipitation ◽  
Extreme Precipitation Events

<p>The Seasonal Trends and Analysis of Residuals (STAR) Empirical-Statistical Downscaling Model (ESDM) is a new bias correction and downscaling method that employs a signal processing approach to decompose observed and model-simulated temperature and precipitation into long-term trends, static and dynamic annual climatologies, and day-to-day variability. It then individually bias-corrects each signal, using a nonparametric Kernel Density Estimation function for the daily anomalies, before reassembling into a coherent time series.</p><p>Comparing the performance of this method in bias-correcting daily temperature and precipitation relative to 25km high-resolution dynamical global model simulations shows significant improvement over commonly-used ESDMs in North America for high and low quantiles of the distribution and overall minimal bias acceptable for all but the most extreme precipitation amounts (beyond the 99.9<sup>th</sup> quantile of wet days) and for temperature at very high elevations during peak historical snowmelt months.</p><p>STAR-ESDM is a MATLAB-based code that minimizes computational demand to enable rapid bias-correction and spatial downscaling of multiple datasets. Here, we describe new CMIP5 and CMIP6-based datasets of daily maximum and minimum temperature and daily precipitation for nearly 10,000 weather stations across North and Central America, as well as gridded datasets for the contiguous U.S., Canada, and globally. In 2022, we plan to extend the station-based downscaling globally as well, since point-source projections can be of use in assessment of climate impacts in many fields, from urban health to water supply.</p><p>The projections have furthermore been translated into a series of impact-relevant indicators at the seasonal,  monthly, and daily scale including multi-day heat waves, extreme precipitation events, threshold exceedences, and cumulative degree-days for individual RCP/ssp scenarios as well as by global mean temperature thresholds as described in Hayhoe et al. (2018; U.S. Fourth National Climate Assessment Volume 1 Chapter 4).</p><p>In this presentation we describe the methodology, briefly highlight results from the evaluation and comparison analysis, and summarize available and forthcoming projections using this computational framework.</p>

scholarly journals Comparison of regional characteristics of land precipitation climatology projected by an MRI-AGCM multi-cumulus scheme and multi-SST ensemble with CMIP5 multi-model ensemble projections

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Rui Ito ◽  
Tosiyuki Nakaegawa ◽  
Izuru Takayabu
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
General Circulation ◽  
Annual Precipitation ◽  
Cmip5 Models ◽  
Climate Projections ◽  
The Tibetan Plateau ◽  
Model Ensemble ◽  
Uncertainty Range ◽  
High Performing

AbstractEnsembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.

scholarly journals Amplified Impact of Climate Change on Fine-Sediment Delivery to a Subsiding Coast, Humboldt Bay, California

2021 ◽  
Author(s):  
Jennifer A. Curtis ◽  
Lorraine E. Flint ◽  
Michelle A. Stern ◽  
Jack Lewis ◽  
Randy D. Klein
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
Baseline Period ◽  
Climate Impacts ◽  
Sediment Supply ◽  
Balance Model ◽  
Climate Projections ◽  
Sediment Delivery ◽  
Beneficial Reuse ◽  
Coastal Margin

AbstractIn Humboldt Bay, tectonic subsidence exacerbates sea-level rise (SLR). To build surface elevations and to keep pace with SLR, the sediment demand created by subsidence and SLR must be balanced by an adequate sediment supply. This study used an ensemble of plausible future scenarios to predict potential climate change impacts on suspended-sediment discharge (Qss) from fluvial sources. Streamflow was simulated using a deterministic water-balance model, and Qss was computed using statistical sediment-transport models. Changes relative to a baseline period (1981–2010) were used to assess climate impacts. For local basins that discharge directly to the bay, the ensemble means projected increases in Qss of 27% for the mid-century (2040–2069) and 58% for the end-of-century (2070–2099). For the Eel River, a regional sediment source that discharges sediment-laden plumes to the coastal margin, the ensemble means projected increases in Qss of 53% for the mid-century and 99% for the end-of-century. Climate projections of increased precipitation and streamflow produced amplified increases in the regional sediment supply that may partially or wholly mitigate sediment demand caused by the combined effects of subsidence and SLR. This finding has important implications for coastal resiliency. Coastal regions with an increasing sediment supply may be more resilient to SLR. In a broader context, an increasing sediment supply from fluvial sources has global relevance for communities threatened by SLR that are increasingly building resiliency to SLR using sediment-based solutions that include regional sediment management, beneficial reuse strategies, and marsh restoration.

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

scholarly journals New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid

2019 ◽  
Vol 58 (12) ◽  
pp. 2617-2632 ◽  
Author(s):  
Qifen Yuan ◽  
Thordis L. Thorarinsdottir ◽  
Stein Beldring ◽  
Wai Kwok Wong ◽  
Shaochun Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Change Signal ◽  
Climate Projections ◽  
Data Product ◽  
Temporal Features ◽  
Stochastic Downscaling

AbstractIn applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

scholarly journals South Asia river-flow projections and their implications for water resources

2015 ◽  
Vol 19 (12) ◽  
pp. 4783-4810 ◽  
Author(s):  
C. Mathison ◽  
A. J. Wiltshire ◽  
P. Falloon ◽  
A. J. Challinor
Keyword(s):  
Water Resources ◽  
South Asia ◽  
River Flow ◽  
Climate Model ◽  
Asian Summer Monsoon ◽  
Regional Climate ◽  
Global Climate Models ◽  
Climate Projections ◽  
The South ◽  
River Flows

Abstract. South Asia is a region with a large and rising population, a high dependence on water intense industries, such as agriculture and a highly variable climate. In recent years, fears over the changing Asian summer monsoon (ASM) and rapidly retreating glaciers together with increasing demands for water resources have caused concern over the reliability of water resources and the potential impact on intensely irrigated crops in this region. Despite these concerns, there is a lack of climate simulations with a high enough resolution to capture the complex orography, and water resource analysis is limited by a lack of observations of the water cycle for the region. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. Two global climate models (GCMs), which represent the ASM reasonably well are downscaled (1960–2100) using a regional climate model (RCM). In the absence of robust observations, ERA-Interim reanalysis is also downscaled providing a constrained estimate of the water balance for the region for comparison against the GCMs (1990–2006). The RCM river flow is routed using a river-routing model to allow analysis of present-day and future river flows through comparison with available river gauge observations. We examine how useful these simulations are for understanding potential changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows but overestimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century. The future maximum river-flow rates still occur during the ASM period, with a magnitude in some cases, greater than the present-day natural variability. Increases in river flow could mean additional water resources for irrigation, the largest usage of water in this region, but has implications in terms of inundation risk. These projected increases could be more than countered by changes in demand due to depleted groundwater, increases in domestic use or expansion of water intense industries. Including missing hydrological processes in the model would make these projections more robust but could also change the sign of the projections.

scholarly journals War or Peace on the Rivers of South Asia?

2020 ◽  
Vol 4 (1) ◽  
pp. 242-254
Author(s):  
Muhammad Imran Mehsud ◽  
Azam Jan ◽  
Tariq Anwar Khan
Keyword(s):  
South Asia ◽  
Water Relations ◽  
South Asian ◽  
The Other ◽  
The South ◽  
Water Sharing ◽  
The Ganges

The renowned water expert, John Briscoe, predicted a bleak future for India-Pakistan water relations across the Indus attributing it to Pakistan’s downstream anxieties vis-à-vis upstream regional hegemon-India. Do the other co-riparian states of India share the same bleak future across the South Asian rivers of the Ganges, Brahmaputra, and Meghna or are the water relations across these rivers peaceful as compared to the Indus? To answer this question, this study first explores India-Pakistan water disputes on the Indus and then analyses India-Bangladesh water disputes on the Ganges and Brahmaputra, India-Nepal, India-Bhutan, and Pakistan-Afghanistan water relations. The methodology adopted for this study is descriptive, historical, and analytical in its nature. The study concludes that India has not only failed to adopt a conciliatory approach towards Pakistan on the Indus but has generated mistrust amongst other neighbouring countries over water sharing due to its hegemonic hydro-behaviour. It recommends that India should adopt a conciliatory approach to have peaceful relations across the rivers of South Asia.

scholarly journals A high-resolution downscaled CMIP6 projections dataset of essential surface climate variables over the globe coherent with the ERA5-Land reanalysis for climate change impact assessments

2021 ◽  
Author(s):  
Thomas Noël ◽  
Harilaos Loukos ◽  
Dimitri Defrance
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Models ◽  
Mapping Method ◽  
Cumulative Distribution ◽  
Climate Projections ◽  
Climate Modelling ◽  
Temperature And Precipitation ◽  
Surface Climate ◽  
Emissions Scenarios

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.1°x 0.1°, comprises 5 climate models and includes two surface daily variables at monthly resolution: air temperature and precipitation. Two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP126) and one without mitigation (SSP585). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modelling community standards and value checking for outlier detection.

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