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

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.

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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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Communicating future climate projections of precipitation change

Climatic Change ◽

10.1007/s10584-021-03118-9 ◽

2021 ◽

Vol 166 (1-2) ◽

Author(s):

Joseph Daron ◽

Susanne Lorenz ◽

Andrea Taylor ◽

Suraje Dessai

Keyword(s):

Decision Making ◽

Online Survey ◽

Climate Models ◽

Climate Model ◽

Precipitation Change ◽

Future Climate ◽

Climate Projections ◽

Ensemble Mean ◽

Future Climate Projections ◽

Made In

AbstractUnderstanding how precipitation may change in the future is important for guiding climate change adaptation. Climate models are the primary tools for providing information on future precipitation change, though communicating and interpreting results of different model simulations is challenging. Using an online survey, completed by producers and users of climate model information, we compare and evaluate interpretations of different approaches used to summarise and visualise future climate projections. Results reveal large differences in interpretations of precipitation change arising from choices made in summarising and visualising the data. Respondents interpret significantly smaller ranges of future precipitation change when provided with the multi-model ensemble mean or percentile information, which are commonly used to summarise climate model projections, compared to information about the full ensemble. The ensemble mean is found to be particularly misleading, even when used with information to show model agreement in the sign of change. We conclude that these approaches can lead to distorted interpretations which may impact on adaptation policy and decision-making. To help improve the interpretation and use of climate projections in decision-making, regular testing of visualisations and sustained engagement with target audiences is required to determine the most effective and appropriate visualisation approaches.

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An Analysis of Regional Climate Simulations for Western Australia’s Wine Regions—Model Evaluation and Future Climate Projections

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-16-0333.1 ◽

2017 ◽

Vol 56 (7) ◽

pp. 2113-2138 ◽

Cited By ~ 8

Author(s):

Rebecca Firth ◽

Jatin Kala ◽

Thomas J. Lyons ◽

Julia Andrys

Keyword(s):

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Global Climate Models ◽

Future Climate ◽

Climate Indices ◽

Climate Projections ◽

Model Errors ◽

Current Climate ◽

Temperature Indices

AbstractThe Weather Research and Forecasting (WRF) Model is evaluated as a regional climate model for the simulation of climate indices that are relevant to viticulture in Western Australia’s wine regions at a 5-km resolution under current and future climate. WRF is driven with ERA-Interim reanalysis for the current climate and three global climate models (GCMs) for both current and future climate. The focus of the analysis is on a selection of climate indices that are commonly used in climate–viticulture research. Simulations of current climate are evaluated against an observational dataset to quantify model errors over the 1981–2010 period. Changes to the indices under future climate based on the SRES A2 emissions scenario are then assessed through an analysis of future (2030–59) minus present (1970–99) climate. Results show that when WRF is driven with ERA-Interim there is generally good agreement with observations for all of the indices although there is a noticeable negative bias for the simulation of precipitation. The results for the GCM-forced simulations were less consistent. Namely, while the GCM-forced simulations performed reasonably well for the temperature indices, all simulations performed inconsistently for the precipitation index. Climate projections showed significant warming for both of the temperature indices and indicated potential risks to Western Australia’s wine growing regions under future climate, particularly in the north. There was disagreement between simulations with regard to the projections of the precipitation indices and hence greater uncertainty as to how these will be characterized under future climate.

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Trends in projections of standardized precipitation indices in a future climate in Poland

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1947-2016 ◽

2016 ◽

Vol 20 (5) ◽

pp. 1947-1969 ◽

Cited By ~ 22

Author(s):

Marzena Osuch ◽

Renata J. Romanowicz ◽

Deborah Lawrence ◽

Wai K. Wong

Keyword(s):

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Standardized Precipitation Index ◽

Future Climate ◽

Future Climate Change ◽

Climate Projections ◽

Monthly Precipitation

Abstract. Possible future climate change effects on dryness conditions in Poland are estimated for six climate projections using the standardized precipitation index (SPI). The time series of precipitation represent six different climate model runs under the selected emission scenario for the period 1971–2099. Monthly precipitation values were used to estimate the SPI for multiple timescales (1, 3, 6, 12, and 24 months) for a spatial resolution of 25 km for the whole country. Trends in the SPI were analysed using the Mann–Kendall test with Sen's slope estimator for each grid cell for each climate model projection and aggregation scale, and results obtained for uncorrected precipitation and bias corrected precipitation were compared. Bias correction was achieved using a distribution-based quantile mapping (QM) method in which the climate model precipitation series were adjusted relative to gridded precipitation data for Poland. The results show that the spatial pattern of the trend depends on the climate model, the timescale considered and on the bias correction. The effect of change on the projected trend due to bias correction is small compared to the variability among climate models. We also summarize the mechanisms underlying the influence of bias correction on trends in precipitation and the SPI using a simple example of a linear bias correction procedure. In both cases, the bias correction by QM does not change the direction of changes but can change the slope of trend, and the influence of bias correction on SPI is much reduced. We also have noticed that the results for the same global climate model, driving different regional climate model, are characterized by a similar pattern of changes, although this behaviour is not seen at all timescales and seasons.

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Realised added value in dynamical downscaling of Australian climate change

10.5194/egusphere-egu21-3855 ◽

2021 ◽

Author(s):

Giovanni Di Virgilio ◽

Jason P. Evans ◽

Alejandro Di Luca ◽

Michael R. Grose ◽

Vanessa Round ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Adaptation ◽

Regional Climate ◽

Future Climate ◽

Added Value ◽

Climate Projections ◽

Eastern Australia ◽

Australian Alps ◽

Future Climate Projections

Coarse resolution global climate models (GCM) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, &#8216;realised added value&#8217;, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.

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Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

10.21203/rs.3.rs-725819/v1 ◽

2021 ◽

Author(s):

Antoine Doury ◽

Samuel Somot ◽

Sébastien Gadat ◽

Aurélien Ribes ◽

Lola Corre

Keyword(s):

Climate Change ◽

Network Architecture ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Future Climate ◽

Near Surface ◽

The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

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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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Comparison of different infiltration schemes in the regional climate model REMO

10.5194/egusphere-egu21-9874 ◽

2021 ◽

Author(s):

Daniel Abel ◽

Katrin Ziegler ◽

Felix Pollinger ◽

Heiko Paeth

Keyword(s):

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Root Zone ◽

Regional Climate ◽

Climate Projections ◽

Soil Hydrology ◽

Spatiotemporal Resolution ◽

Development Fund ◽

Near Surface

The European Regional Development Fund-Project BigData@Geo aims to create highly resolved climate projections for the model region of Lower Franconia in Bavaria, Germany. These projections are analyzed and made available to local stakeholders of agriculture, forestry, and viniculture as well as general public. Since regional climate models&#8217; spatiotemporal resolution often is too coarse to deal with such local issues, the regional climate model REMO is improved within the frame of the project in cooperation with the Climate Service Center Germany (GERICS).Accurate and highly resolved climate projections require realistic modeling of soil hydrology. Thus, REMO&#8217;s original bucket scheme is replaced by a 5-layer soil scheme. It allows for the representation of water below the root zone. Evaporation is possible solely from the top layer instead of the entire bucket and water can flow vertically between the layers. Consequently, the properties and processes change significantly compared to the bucket scheme. Both, the bucket and the 5-layer scheme, use the improved Arno scheme to separate throughfall into infiltration and surface runoff.In this study, we examine if this scheme is suitable for use with the improved soil hydrology or if other schemes lead to better results. For this, we (1) modify the improved Arno scheme and further introduce the infiltration equations of (2) Philip as well as (3) Green and Ampt. First results of the comparison of these four different schemes and their influence on soil moisture and near-surface atmospheric variables are presented.

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Back to the Future: Using Long-Term Observational and Paleo-Proxy Reconstructions to Improve Model Projections of Antarctic Climate

Geosciences ◽

10.3390/geosciences9060255 ◽

2019 ◽

Vol 9 (6) ◽

pp. 255 ◽

Cited By ~ 6

Author(s):

Thomas J. Bracegirdle ◽

Florence Colleoni ◽

Nerilie J. Abram ◽

Nancy A. N. Bertler ◽

Daniel A. Dixon ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Ice Core ◽

Global Climate Models ◽

Climate Projections ◽

Past Performance ◽

Wide Range ◽

Proxy Reconstructions

Quantitative estimates of future Antarctic climate change are derived from numerical global climate models. Evaluation of the reliability of climate model projections involves many lines of evidence on past performance combined with knowledge of the processes that need to be represented. Routine model evaluation is mainly based on the modern observational period, which started with the establishment of a network of Antarctic weather stations in 1957/58. This period is too short to evaluate many fundamental aspects of the Antarctic and Southern Ocean climate system, such as decadal-to-century time-scale climate variability and trends. To help address this gap, we present a new evaluation of potential ways in which long-term observational and paleo-proxy reconstructions may be used, with a particular focus on improving projections. A wide range of data sources and time periods is included, ranging from ship observations of the early 20th century to ice core records spanning hundreds to hundreds of thousands of years to sediment records dating back 34 million years. We conclude that paleo-proxy records and long-term observational datasets are an underused resource in terms of strategies for improving Antarctic climate projections for the 21st century and beyond. We identify priorities and suggest next steps to addressing this.

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Setup of the PMIP3 paleoclimate experiments conducted using an Earth System Model, MIROC-ESM

Geoscientific Model Development Discussions ◽

10.5194/gmdd-5-2527-2012 ◽

2012 ◽

Vol 5 (3) ◽

pp. 2527-2569 ◽

Cited By ~ 1

Author(s):

T. Sueyoshi ◽

R. Ohgaito ◽

A. Yamamoto ◽

M. O. Chikamoto ◽

T. Hajima ◽

...

Keyword(s):

Climate Model ◽

Coupled Model ◽

Earth System Model ◽

Future Climate ◽

System Model ◽

Climate Feedback ◽

Climate Projections ◽

Earth System ◽

Intergovernmental Panel ◽

Intercomparison Project

Abstract. The importance of climate model evaluation using paleoclimate simulations for better future climate projections has been recognized by the Intergovernmental Panel on Climate Change. In recent years, Earth System Models (ESMs) were developed to investigate carbon-cycle climate feedback, as well as to project the future climate. Paleoclimate events, especially those associated with the variations in atmospheric CO2 level or land vegetation, provide suitable benchmarks to evaluate ESMs. Here we present implementations of the paleoclimate experiments proposed by the Coupled Model Intercomparison Project phase 5/Paleoclimate Modelling Intercomparison Project phase 3 (CMIP5/PMIP3) using an Earth System Model, MIROC-ESM. In this paper, experimental settings and procedures of the mid-Holocene, the Last Glacial Maximum, and the Last Millennium experiments are explained. The first two experiments are time slice experiments and the last one is a transient experiment. The complexity of the model requires various steps to correctly configure the experiments. Several basic outputs are also shown.

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