Hydrologic impact of regional climate change for the snow-fed and glacier-fed river basins in the Republic of Tajikistan: statistical downscaling of global climate model projections

There is strong correlative evidence that human-induced climate warming is contributing to changes in the timing of natural events. Firm attribution, however, requires cause-and-effect links between observed climate change and altered phenology, together with statistical confidence that observed regional climate change is anthropogenic. We provide evidence for phenological shifts in the butterfly Heteronympha merope in response to regional warming in the southeast Australian city of Melbourne. The mean emergence date for H. merope has shifted −1.5 days per decade over a 65-year period with a concurrent increase in local air temperatures of approximately 0.16°C per decade. We used a physiologically based model of climatic influences on development, together with statistical analyses of climate data and global climate model projections, to attribute the response of H. merope to anthropogenic warming. Such mechanistic analyses of phenological responses to climate improve our ability to forecast future climate change impacts on biodiversity.

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Downscaling a global climate model to simulate climate change over the US and the implication on regional and urban air quality

Geoscientific Model Development ◽

10.5194/gmd-6-1429-2013 ◽

2013 ◽

Vol 6 (5) ◽

pp. 1429-1445 ◽

Cited By ~ 28

Author(s):

M. Trail ◽

A. P. Tsimpidi ◽

P. Liu ◽

K. Tsigaridis ◽

Y. Hu ◽

...

Keyword(s):

Climate Change ◽

Air Quality ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Climate Conditions ◽

Southeastern Us ◽

The Us ◽

Ozone Levels

Abstract. Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with the Weather Research and Forecasting (WRF) model to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the contiguous United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF regional climate model (RCM) to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12 km by 12 km resolution, as well as the effect of evolving climate conditions on the air quality at major US cities. The high-resolution simulations produce somewhat different results than the coarse-resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the US during fall (western US, Texas, northeastern, and southeastern US), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (Northeast). Changes in regional climate that would enhance ozone levels are increased temperatures and stagnation along with decreased precipitation and ventilation. We also find that daily peak temperatures tend to increase in most major cities in the US, which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

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Brief Communication: An update of the article "Modelling flood damages under climate change conditions – a case study for Germany"

Natural Hazards and Earth System Science ◽

10.5194/nhess-16-1617-2016 ◽

2016 ◽

Vol 16 (7) ◽

pp. 1617-1622 ◽

Cited By ~ 8

Author(s):

Fred Fokko Hattermann ◽

Shaochun Huang ◽

Olaf Burghoff ◽

Peter Hoffmann ◽

Zbigniew W. Kundzewicz

Keyword(s):

Climate Change ◽

Large Scale ◽

Considerable Increase ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Impact Modelling ◽

Flood Damages

Abstract. In our first study on possible flood damages under climate change in Germany, we reported that a considerable increase in flood-related losses can be expected in a future warmer climate. However, the general significance of the study was limited by the fact that outcome of only one global climate model (GCM) was used as a large-scale climate driver, while many studies report that GCMs are often the largest source of uncertainty in impact modelling. Here we show that a much broader set of global and regional climate model combinations as climate drivers show trends which are in line with the original results and even give a stronger increase of damages.

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Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽

10.3390/ecws-3-05819 ◽

2018 ◽

Vol 7 (1) ◽

pp. 23 ◽

Cited By ~ 1

Author(s):

Carlos Garijo ◽

Luis Mediero

Keyword(s):

Climate Change ◽

Iberian Peninsula ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Control Period ◽

Regional Climate Models ◽

The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

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Brief Communication: An update of the article "Modeling flood damages under climate change conditions – a case study for Germany"

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-3-7231-2015 ◽

2015 ◽

Vol 3 (12) ◽

pp. 7231-7245

Author(s):

F. F. Hattermann ◽

S. Huang ◽

O. Burghoff ◽

P. Hoffmann ◽

Z. W. Kundzewicz

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Large Scale ◽

Considerable Increase ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Flood Damages

Abstract. In our first study on possible flood damages under climate change in Germany, we reported that a considerable increase in flood related losses can be expected in future, warmer, climate. However, the general significance of the study was limited by the fact that outcome of only one Global Climate Model (GCM) was used as large scale climate driver, while many studies report that GCM models are often the largest source of uncertainty in impact modeling. Here we show that a much broader set of global and regional climate model combinations as climate driver shows trends which are in line with the original results and even give a stronger increase of damages.

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Downscaling a global climate model to simulate climate change impacts on US regional and urban air quality

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-2517-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 2517-2549 ◽

Cited By ~ 1

Author(s):

M. Trail ◽

A. P. Tsimpidi ◽

P. Liu ◽

K. Tsigaridis ◽

Y. Hu ◽

...

Keyword(s):

Climate Change ◽

Air Quality ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Climate Change Impacts ◽

Climate Conditions ◽

Southeastern Us ◽

The Us

Abstract. Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with WRF to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the continental United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF RCM to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12 km by 12 km resolution, as well as the effect of evolving climate conditions on the air quality at major US cities. The high resolution simulations produce somewhat different results than the coarse resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the US during fall (Western US, Texas, Northeastern, and Southeastern US), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (northeast). We also find that daily peak temperatures tend to increase in most major cities in the US which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

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Is time a variable like the others in multivariate statistical downscaling and bias correction?

Earth System Dynamics ◽

10.5194/esd-12-1253-2021 ◽

2021 ◽

Vol 12 (4) ◽

pp. 1253-1273

Author(s):

Yoann Robin ◽

Mathieu Vrac

Keyword(s):

Statistical Downscaling ◽

Bias Correction ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Synthetic Data ◽

The Other ◽

Strong Increase ◽

Temporal Properties

Abstract. Bias correction and statistical downscaling are now regularly applied to climate simulations to make then more usable for impact models and studies. Over the last few years, various methods were developed to account for multivariate – inter-site or inter-variable – properties in addition to more usual univariate ones. Among such methods, temporal properties are either neglected or specifically accounted for, i.e. differently from the other properties. In this study, we propose a new multivariate approach called “time-shifted multivariate bias correction” (TSMBC), which aims to correct the temporal dependency in addition to the other marginal and multivariate aspects. TSMBC relies on considering the initial variables at various times (i.e. lags) as additional variables to be corrected. Hence, temporal dependencies (e.g. auto-correlations) to be corrected are viewed as inter-variable dependencies to be adjusted and an existing multivariate bias correction (MBC) method can then be used to answer this need. This approach is first applied and evaluated on synthetic data from a vector auto-regressive (VAR) process. In a second evaluation, we work in a “perfect model” context where a regional climate model (RCM) plays the role of the (pseudo-)observations, and where its forcing global climate model (GCM) is the model to be downscaled or bias corrected. For both evaluations, the results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted. However, increasing the number of lags too much does not necessarily improve the temporal properties, and an overly strong increase in the number of dimensions of the dataset to be corrected can even imply some potential instability in the adjusted and/or downscaled results, calling for a reasoned use of this approach for large datasets.

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Building synergies in regional climate services for Southeast Asia: The ASEAN Regional Climate Data, Analysis and Projections (ARCDAP) workshop series.

10.5194/egusphere-egu2020-15094 ◽

2020 ◽

Author(s):

Gerald Lim ◽

Aurel Moise ◽

Raizan Rahmat ◽

Bertrand Timbal

Keyword(s):

Climate Change ◽

Data Analysis ◽

Southeast Asia ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Early Warning Systems ◽

Climate Data ◽

Climate Services

Southeast Asia (SEA) is a rapidly developing and densely populated region that is home to over 600 million people. This, together with the region&#8217;s high sensitivity, exposure and low adaptive capacities, makes it particularly vulnerable to climate change and extremes such as floods, droughts and tropical cyclones. While the last decade saw some countries in SEA develop their own climate change projections, studies were largely uncoordinated and most countries still lack the capability to independently produce robust future climate information. Following a proposal from the World Meteorological Organisation (WMO) Regional Association (RA) V working group on climate services, the ASEAN Regional Climate Data, Analysis and Projections (ARCDAP) workshop series was conceived in 2017 to bridge these gaps in regional synergies. The ARCDAP series has been organised annually since 2018 by the ASEAN Specialised Meteorological Centre (hosted by Meteorological Service Singapore) with support from WMO through the Canada-funded Climate Risk and Early Warning Systems (Canada-CREWS) initiative.This presentation will cover the activities and outcomes from the first two workshops, as well as the third which will be held in February 2020. The ARCDAP series has so far brought together representatives from ASEAN National Meteorological and Hydrological Services (NMHSs), climate scientists and end-users from policy-making and a variety of vulnerability and impact assessment (VIA) sectors, to discuss and identify best practices regarding the delivery of climate change information, data usage and management, advancing the science etc. Notable outputs include two comprehensive workshop reports and a significant regional contribution to the HadEX3 global land in-situ-based dataset of temperature and precipitation extremes, motivated by work done with the ClimPACT2 software.The upcoming third workshop will endeavour to encourage the uptake of the latest ensemble of climate simulations from the Coupled Model Intercomparison Project (CMIP6) using CMIP-endorsed tools such as ESMValTool. This will address the need for ASEAN climate change practitioners to upgrade their knowledge of the latest global climate model database. It is anticipated that with continued support from WMO, the series will continue with the Fourth workshop targeting the assessment of downscaling experiments in 2021.

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Estimates of Twenty-First-Century Flood Risk in the Pacific Northwest Based on Regional Climate Model Simulations

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0137.1 ◽

2014 ◽

Vol 15 (5) ◽

pp. 1881-1899 ◽

Cited By ~ 56

Author(s):

Eric P. Salathé ◽

Alan F. Hamlet ◽

Clifford F. Mass ◽

Se-Yeun Lee ◽

Matt Stumbaugh ◽

...

Keyword(s):

Pacific Northwest ◽

Statistical Downscaling ◽

Flood Risk ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Hydrologic Model ◽

The Pacific ◽

Physically Based

Abstract Results from a regional climate model simulation show substantial increases in future flood risk (2040–69) in many Pacific Northwest river basins in the early fall. Two primary causes are identified: 1) more extreme and earlier storms and 2) warming temperatures that shift precipitation from snow to rain dominance over regional terrain. The simulations also show a wide range of uncertainty among different basins stemming from localized storm characteristics. While previous research using statistical downscaling suggests that many areas in the Pacific Northwest are likely to experience substantial increases in flooding in response to global climate change, these initial estimates do not adequately represent the effects of changes in heavy precipitation. Unlike statistical downscaling techniques applied to global climate model scenarios, the regional model provides an explicit, physically based simulation of the seasonality, size, location, and intensity of historical and future extreme storms, including atmospheric rivers. This paper presents climate projections from the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) global climate model dynamically downscaled using the Weather Research and Forecasting (WRF) Model implemented at 12-km resolution for the period 1970–2069. The resulting daily precipitation and temperature data are bias corrected and used as input to a physically based Variable Infiltration Capacity (VIC) hydrologic model. From the daily time step simulations of streamflow produced by the hydrologic model, probability distributions are fit to the extreme events extracted from each water year and flood statistics for various return intervals are estimated.

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