Enhancing the Output of Climate Models: A Weather Generator for Climate Change Impact Studies

Evaluation of effects of climate change on climate variable extremes is a key topic in civil and structural engineering, strongly affecting adaptation strategy for resilience. Appropriate procedures to assess the evolution over time of climatic actions are needed to deal with the inherent uncertainty of climate projections, also in view of providing more sound and robust predictions at the local scale. In this paper, an ad hoc weather generator is presented that is able to provide a quantification of climate model inherent uncertainties. Similar to other weather generators, the proposed algorithm allows the virtualization of the climatic data projection process, overcoming the usual limitations due to the restricted number of available climate model runs, requiring huge computational time. However, differently from other weather generation procedures, this new tool directly samples from the output of Regional Climate Models (RCMs), avoiding the introduction of additional hypotheses about the stochastic properties of the distributions of climate variables. Analyzing the ensemble of so-generated series, future changes of climatic actions can be assessed, and the associated uncertainties duly estimated, as a function of considered greenhouse gases emission scenarios. The efficiency of the proposed weather generator is discussed evaluating performance metrics and referring to a relevant case study: the evaluation of extremes of minimum and maximum temperature, precipitation, and ground snow load in a central Eastern region of Italy, which is part of the Mediterranean climatic zone. Starting from the model ensemble of six RCMs, factors of change uncertainty maps for the investigated region are derived concerning extreme daily temperatures, daily precipitation, and ground snow loads, underlying the potentialities of the proposed approach.

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Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty

Geospatial health ◽

10.4081/gh.2016.393 ◽

2016 ◽

Vol 11 (1s) ◽

Cited By ~ 8

Author(s):

Joseph Leedale ◽

Adrian M. Tompkins ◽

Cyril Caminade ◽

Anne E. Jones ◽

Grigory Nikulin ◽

...

Keyword(s):

Climate Change ◽

Malaria Transmission ◽

Climate Models ◽

Climate Model ◽

Eastern Africa ◽

Future Climate Change ◽

Climate Projections ◽

Model Ensemble ◽

East African Community ◽

Recent Climate

The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.

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Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

10.5194/egusphere-egu21-12054 ◽

2021 ◽

Author(s):

Gaby S. Langendijk ◽

Diana Rechid ◽

Daniela Jacob

Keyword(s):

Climate Change ◽

Urban Areas ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Climate Projections ◽

Climate Data ◽

Urban Rural

Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2&#176;C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.

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Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽

10.3390/w11112266 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2266 ◽

Cited By ~ 4

Author(s):

Enrique Soriano ◽

Luis Mediero ◽

Carlos Garijo

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Control Period ◽

Flood Frequency ◽

Climate Projections ◽

The Future ◽

Flood Quantiles ◽

The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

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Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-15-0004.1 ◽

2017 ◽

Vol 98 (1) ◽

pp. 79-93 ◽

Cited By ~ 121

Author(s):

Elizabeth J. Kendon ◽

Nikolina Ban ◽

Nigel M. Roberts ◽

Hayley J. Fowler ◽

Malcolm J. Roberts ◽

...

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Global Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Projections ◽

Convective Storms ◽

Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

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

Water ◽

10.3390/w10080978 ◽

2018 ◽

Vol 10 (8) ◽

pp. 978 ◽

Cited By ~ 3

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.

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Quantifying the Likelihood of Regional Climate Change: A Hybridized Approach

Journal of Climate ◽

10.1175/jcli-d-11-00730.1 ◽

2013 ◽

Vol 26 (10) ◽

pp. 3394-3414 ◽

Cited By ~ 18

Author(s):

C. Adam Schlosser ◽

Xiang Gao ◽

Kenneth Strzepek ◽

Andrei Sokolov ◽

Chris E. Forest ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Projections ◽

Massachusetts Institute Of Technology ◽

Adaptation To Climate Change ◽

Intergovernmental Panel ◽

Near Surface ◽

Institute Of Technology

Abstract The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: specifically, the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, the authors present a technique that extends the latitudinal projections of the 2D atmospheric model of the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate model projections archived from exercises carried out for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and this approach is demonstrated in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, the authors characterize the climate models’ spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of metaensembles of regional climate outcomes, combining the ensembles of the MIT IGSM—which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios—with regionally resolved patterns from the archived IPCC climate model projections. This hybridization of the climate model longitudinal projections with the global and latitudinal patterns projected by the IGSM can, in principle, be applied to any given state or flux variable that has the sufficient observational and model-based information.

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Climate Change Impact Assessment for Aji Basin Using Statistical Downscaling and Bias Correction of Climate Model Outputs

Current World Environment ◽

10.12944/cwe.11.2.40 ◽

2016 ◽

Vol 11 (2) ◽

pp. 670-678 ◽

Cited By ~ 1

Author(s):

N. S Vithlani ◽

H. D Rank

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Regional Climate ◽

Climate Extremes ◽

Global Climate Models ◽

Climate Indices ◽

Climate Projections

For the future projections Global climate models (GCMs) enable development of climate projections and relate greenhouse gas forcing to future potential climate states. When focusing it on smaller scales it exhibit some limitations to overcome this problem, regional climate models (RCMs) and other downscaling methods have been developed. To ensure statistics of the downscaled output matched the corresponding statistics of the observed data, bias correction was used. Quantify future changes of climate extremes were analyzed, based on these downscaled data from two RCMs grid points. Subset of indices and models, results of bias corrected model output and raw for the present day climate were compared with observation, which demonstrated that bias correction is important for RCM outputs. Bias correction directed agreements of extreme climate indices for future climate it does not correct for lag inverse autocorrelation and fraction of wet and dry days. But, it was observed that adjusting both the biases in the mean and variability, relatively simple non-linear correction, leads to better reproduction of observed extreme daily and multi-daily precipitation amounts. Due to climate change temperature and precipitation will increased day by day.

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Catchment-scale assessment of future changes in flood regimes in Norway using stochastic weather generation based on GCM output

10.5194/egusphere-egu2020-13350 ◽

2020 ◽

Author(s):

Deborah Lawrence ◽

Abdelkader Mezghani ◽

Marie Pontopiddan ◽

Rasmus Benestad ◽

Kajsa Parding ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Weather Generator ◽

Temperature Data ◽

Temperature Series ◽

Maximum Temperature ◽

Catchment Scale ◽

Noise Component ◽

Stochastic Weather Generation ◽

Precipitation And Temperature

Assessment of climate change impacts on hydrological processes is often based on simulations driven by precipitation and temperature series derived from bias-adjusted output from Regional Climate Models (RCMs) using boundary conditions from Global Climate Models (GCMs).&#160; This procedure gives, in principle, locally &#8216;correct&#8217; results, but is also very demanding of time and resources. In some cases, the dynamical downscaling (i.e. RCM) followed by bias adjustment procedures fails to preserve the climate change signal found in the underlying GCM simulations, thus undermining the reliability of the resulting hydrological simulations. As an alternative, we have used the stochastic weather generator D2Gen (Mezghani and Hingray, 2009, J. Hydrol., 377(3&#8211;4): 245&#8211;60) to create multiple realisations of catchment-scale precipitation and temperature data series directly from two GCMs (MPI-ESM-LR and NorESM-M1) for the period 1951-2100. D2Gen builds on a suite of Generalised Linear Models (GLMs) to generate precipitation and temperature (i.e. predictands) as a function of explanatory climate variables (or predictors) derived from the GCM such as surface temperature, sea level pressure, westerly and zonal wind components, relative humidity and total precipitation. In this study, we have applied D2Gen on area-averaged precipitation and temperature data for 18 hydrological catchments distributed across Norway. Weather generation is then undertaken based on the expected mean modelled by the GLM plus a noise component to account for local features and random effects introduced by local physical processes that are otherwise not accounted for.&#160; The weather generator was trained for each catchment based on observed precipitation and temperature series for the period 1985-2014, and stochastic weather generation was then performed to construct catchment-scale precipitation and temperature series for the period 1951-2100 that were further used in hydrological simulations based on the HBV hydrological model for the 18 catchments.&#160;Validation of the D2Gen results was based on comparisons with observed annual, seasonal and maximum temperature and precipitation, as well as with observed average annual and maximum annual discharge using 30-year time slices.&#160; Comparisons were also made with projected changes generated from hydrological simulations based on a) EURO-CORDEX RCM simulations (MPI-ESM-LR_SMHI-RCA4 and MPI_CCLM-CM5) for the MPI GCM; and b) high resolution (4 km) simulations with the WRF model driven by a bias-corrected NorESM GCM.&#160; Results suggest that in most catchments the D2gen approach performs equally well or sometimes even better than the traditional &#8216;bias-corrected RCM approach&#8217; in reproducing the 30-year average annual flood during the historical period. We also found that for the projection period, the simulations based directly on the GCM output (via d2gen) tend to give slightly larger projected increases in the average annual flood in rainfall-dominated catchments than does the use of bias-corrected RCM simulations. Overall, the results indicate that the D2Gen weather generator offers a feasible alternative approach for projecting catchment-scale impacts on changes in flood regimes under a changing climate.&#160; It also offers the significant advantage that it can be used directly with the CMIP-6 ensemble of GCMs without the time delay associated with the production of the next round of EURO-CORDEX based simulations.

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Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies

Hydrology Research ◽

10.2166/nh.2010.004 ◽

2010 ◽

Vol 41 (3-4) ◽

pp. 211-229 ◽

Cited By ~ 137

Author(s):

Wei Yang ◽

Johan Andréasson ◽

L. Phil Graham ◽

Jonas Olsson ◽

Jörgen Rosberg ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Climate Projections ◽

Impact Studies ◽

Emissions Scenarios ◽

Precipitation And Temperature

As climate change could have considerable influence on hydrology and corresponding water management, appropriate climate change inputs should be used for assessing future impacts. Although the performance of regional climate models (RCMs) has improved over time, systematic model biases still constrain the direct use of RCM output for hydrological impact studies. To address this, a distribution-based scaling (DBS) approach was developed that adjusts precipitation and temperature from RCMs to better reflect observations. Statistical properties, such as daily mean, standard deviation, distribution and frequency of precipitation days, were much improved for control periods compared to direct RCM output. DBS-adjusted precipitation and temperature from two IPCC Special Report on Emissions Scenarios (SRESA1B) transient climate projections were used as inputs to the HBV hydrological model for several river basins in Sweden for the period 1961–2100. Hydrological results using DBS were compared to results with the widely-used delta change (DC) approach for impact studies. The general signal of a warmer and wetter climate was obtained using both approaches, but use of DBS identified differences between the two projections that were not seen with DC. The DBS approach is thought to better preserve the future variability produced by the RCM, improving usability for climate change impact studies.

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Climate Projections for Precipitation and Temperature Indicators in the Douro Wine Region: The Importance of Bias Correction

Agronomy ◽

10.3390/agronomy11050990 ◽

2021 ◽

Vol 11 (5) ◽

pp. 990

Author(s):

Joana Martins ◽

Helder Fraga ◽

André Fonseca ◽

João Andrade Santos

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Model ◽

Correction Method ◽

Maximum Temperature ◽

Lower Sensitivity ◽

Climate Projections ◽

Quantile Mapping ◽

Bias Correction Method ◽

Wine Region

The implications of weather and climate extremes on the viticulture and winemaking sector can be particularly detrimental and acquire more relevance under a climate change context. A four-member ensemble of the Regional Climate Model-Global Climate Model chain simulations is used to evaluate the potential impacts of climate change on indices of extreme temperature and precipitation, as well as on agroclimatic indices of viticultural suitability in the Douro Wine Region, Portugal, under current and future climate conditions, following the RCP8.5 anthropogenic radiative forcing scenario. Historical (1989–2005) and future (2051–2080) periods are considered for this purpose. Although model outputs are bias-corrected to improve the accuracy of the results, owing to the sensitivity of the climatic indicators to the specific bias correction method, the performance of the linear and quantile mapping methods are compared. The results hint at the importance of choosing the most accurate method (quantile mapping), not only in replicating extremes events but also in reproducing the accumulated agroclimatic indices. Significant differences between the bias correction methods are indeed found for the number of extremely warm days (maximum temperature > 35 °C), number of warm spells, number of warm spell days, number of consecutive dry days, the Dryness Index, and growing season precipitation. The Huglin Index reveals lower sensitivity, thus being more robust to the choice of the method. Hence, an unsuitable bias correction method may hinder the accuracy of climate change projections in studies heavily relying on derived extreme indices and agroclimatic indicators, such as in viticulture. Regarding the climate change signal, significant warming and drying trends are projected throughout the target region, which is supported by previous studies, but also accompanied by an increase of intensity, frequency, and duration of extreme events, namely heatwaves and dry spells. These findings thereby corroborate the need to adopt timely and effective adaptation strategies by the regional winemaking sector to warrant its future sustainability and enhance climate resilience.

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